Athero-Express Biobank Study – IL6 and MCP1 plaque levels.
An ‘Athero-Express Biobank Study’ project
Sander W. van der Laan, PhD | @swvanderlaan; Marios Georgakis; Rainer Malik; Martin Dichgans
2020-06-27
Preparation
Clean the environment.
Set locations, and the working directory.
A package-installation function.
Load those packages.
We will create a datestamp and define the Utrecht Science Park Colour Scheme.
# Function to grep data from glm()/lm()
GLM.CON <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' .\n")
if (nrow(summary(fit)$coefficients) == 1) {
output = c(DATASET, x_name, y, rep(NA,8))
cat("Model not fitted; probably singular.\n")
}else {
cat("Collecting data.\n\n")
effectsize = summary(fit)$coefficients[2,1]
SE = summary(fit)$coefficients[2,2]
OReffect = exp(summary(fit)$coefficients[2,1])
CI_low = exp(effectsize - 1.96 * SE)
CI_up = exp(effectsize + 1.96 * SE)
tvalue = summary(fit)$coefficients[2,3]
pvalue = summary(fit)$coefficients[2,4]
R = summary(fit)$r.squared
R.adj = summary(fit)$adj.r.squared
sample_size = nrow(model.frame(fit))
AE_N = AEDB.CEA.samplesize
Perc_Miss = 100 - ((sample_size * 100)/AE_N)
output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, tvalue, pvalue, R, R.adj, AE_N, sample_size, Perc_Miss)
if (verbose == TRUE) {
cat("We have collected the following and summarize it in an object:\n")
cat("Dataset...................:", DATASET, "\n")
cat("Score/Exposure/biomarker..:", x_name, "\n")
cat("Trait/outcome.............:", y, "\n")
cat("Effect size...............:", round(effectsize, 6), "\n")
cat("Standard error............:", round(SE, 6), "\n")
cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
cat("T-value...................:", round(tvalue, 6), "\n")
cat("P-value...................:", signif(pvalue, 8), "\n")
cat("R^2.......................:", round(R, 6), "\n")
cat("Adjusted r^2..............:", round(R.adj, 6), "\n")
cat("Sample size of AE DB......:", AE_N, "\n")
cat("Sample size of model......:", sample_size, "\n")
cat("Missing data %............:", round(Perc_Miss, 6), "\n")
} else {
cat("Collecting data in summary object.\n")
}
}
return(output)
print(output)
}
GLM.BIN <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' ...\n")
if (nrow(summary(fit)$coefficients) == 1) {
output = c(DATASET, x_name, y, rep(NA,9))
cat("Model not fitted; probably singular.\n")
}else {
cat("Collecting data...\n")
effectsize = summary(fit)$coefficients[2,1]
SE = summary(fit)$coefficients[2,2]
OReffect = exp(summary(fit)$coefficients[2,1])
CI_low = exp(effectsize - 1.96 * SE)
CI_up = exp(effectsize + 1.96 * SE)
zvalue = summary(fit)$coefficients[2,3]
pvalue = summary(fit)$coefficients[2,4]
dev <- fit$deviance
nullDev <- fit$null.deviance
modelN <- length(fit$fitted.values)
R.l <- 1 - dev / nullDev
R.cs <- 1 - exp(-(nullDev - dev) / modelN)
R.n <- R.cs / (1 - (exp(-nullDev/modelN)))
sample_size = nrow(model.frame(fit))
AE_N = AEDB.CEA.samplesize
Perc_Miss = 100 - ((sample_size * 100)/AE_N)
output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, zvalue, pvalue, R.l, R.cs, R.n, AE_N, sample_size, Perc_Miss)
if (verbose == TRUE) {
cat("We have collected the following and summarize it in an object:\n")
cat("Dataset...................:", DATASET, "\n")
cat("Score/Exposure/biomarker..:", x_name, "\n")
cat("Trait/outcome.............:", y, "\n")
cat("Effect size...............:", round(effectsize, 6), "\n")
cat("Standard error............:", round(SE, 6), "\n")
cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
cat("Z-value...................:", round(zvalue, 6), "\n")
cat("P-value...................:", signif(pvalue, 8), "\n")
cat("Hosmer and Lemeshow r^2...:", round(R.l, 6), "\n")
cat("Cox and Snell r^2.........:", round(R.cs, 6), "\n")
cat("Nagelkerke's pseudo r^2...:", round(R.n, 6), "\n")
cat("Sample size of AE DB......:", AE_N, "\n")
cat("Sample size of model......:", sample_size, "\n")
cat("Missing data %............:", round(Perc_Miss, 6), "\n")
} else {
cat("Collecting data in summary object.\n")
}
}
return(output)
print(output)
}Background
Using a Mendelian Randomization approach, we recently examined associations between the circulating levels of 41 cytokines and growth factors and the risk of stroke in the MEGASTROKE GWAS dataset (67,000 stroke cases and 450,000 controls) and found Monocyte chemoattractant protein-1 (MCP-1) as the cytokine showing the strongest association with stroke, particularly large artery and cardioembolic stroke (Georgakis et al., 2019a). Genetically elevated MCP-1 levels were also associated with a higher risk of coronary artery disease and myocardial infarction (Georgakis et al., 2019a). Further, in a meta-analysis of 6 observational population-based of longitudinal cohort studies we recently showed that baseline levels of MCP-1 were associated with a higher risk of ischemic stroke over follow-up (Georgakis et al., 2019b). While these data suggest a central role of MCP-1 in the pathogenesis of atherosclerosis, it remains unknown if MCP-1 levels in the blood really reflect MCP-1 activity. MCP-1 is expressed in the atherosclerotic plaque and attracts monocytes in the subendothelial space (Nelken et al., 1991; Papadopoulou et al., 2008; Takeya et al., 1993; Wilcox et al., 1994). Thus, MCP-1 levels in the plaque might more strongly reflect MCP-1 signaling. However, it remains unknown if MCP-1 plaque levels associate with plaque vulnerability or risk of cardiovascular events.
Objectives
Against this background we now aim to make use of the data from Athero-Express Biobank Study to explore the associations of MCP-1 protein levels in the atherosclerotic plaques from patients undergoing carotid endarterectomy with phenotypes of plaque vulnerability and secondary vascular events over a follow-up of three years.
Methods
Blood
OLINK-platform
- IL6: Interleukin 6. Entrez Gene: 3569. OLINK, plasma
- MCP1: Monocyte chemotactic protein 1, MCP-1 (Chemokine (C-C motif) ligand 2, CCL2). Entrez Gene: 6347. OLINK, plasma
THESE DATA ARE NOT AVAILABLE YET
Plaque
Luminex-platform, measured by Luminex
- MCP1: Monocyte chemotactic protein 1 (a.k.a. CCL2; Entrez Gene: 6347) concentration in plaque [pg/ug]. Measured in two experiments, variables
MCP1andMCP1_pg_ug_2015. We consider the latter the best possible measurement as this was corrected for plaque total protein concentration. - IL6: Interleuking 6 (IL6; Entrez Gene: 3569) concentration in plaque [pg/ug].
- IL6R: Interleuking 6 receptor (IL6R; Entrez Gene: 3570) concentration in plaque [pg/ug].
FACS platform
- IL6: Interleukin 6. Entrez Gene: 3569. Bender MedSystems; cat.nr.: BMS810FF. Recalculated FACS. [pg/mL]
Loading data
Clinical data
Loading Athero-Express clinical data.
require(haven)
# AEDB <- haven::read_sav(paste0(AEDB_loc, "/2019-3NEW_AtheroExpressDatabase_ScientificAE_02072019_IC_added.sav"))
AEDB <- haven::read_sav(paste0(AEDB_loc, "/2020_1_NEW_AtheroExpressDatabase_ScientificAE_16-03-2020.sav"))
head(AEDB)NA
NA
NAExamine AEDB
We can examine the contents of the Athero-Express Biobank dataset to know what each variable is called, what class (type) it has, and what the variable description is.
There is an excellent post on this: https://www.r-bloggers.com/working-with-spss-labels-in-r/.
AEDB %>% sjPlot::view_df(show.type = TRUE,
show.frq = TRUE,
show.prc = TRUE,
show.na = TRUE,
max.len = TRUE,
wrap.labels = 20,
verbose = FALSE,
use.viewer = FALSE,
file = paste0(OUT_loc, "/", Today, ".AEDB.dictionary.html")) Fixing and creating variables
We need to be very strict in defining symptoms. Therefore we will fix a new variable that groups symptoms at inclusion.
Coding of symptoms is as follows:
- missing -999
- Asymptomatic 0
- TIA 1
- minor stroke 2
- Major stroke 3
- Amaurosis fugax 4
- Four vessel disease 5
- Vertebrobasilary TIA 7
- Retinal infarction 8
- Symptomatic, but aspecific symtoms 9
- Contralateral symptomatic occlusion 10
- retinal infarction 11
- armclaudication due to occlusion subclavian artery, CEA needed for bypass 12
- retinal infarction + TIAs 13
- Ocular ischemic syndrome 14
- ischemisch glaucoom 15
- subclavian steal syndrome 16
- TGA 17
We will group as follows in Symptoms.5G:
- Asymptomatic > 0
- TIA > 1, 7, 13
- Stroke > 2, 3
- Ocular > 4, 14, 15
- Retinal infarction > 8, 11
- Other > 5, 9, 10, 12, 16, 17
We will also group as follows in AsymptSympt:
- Asymptomatic > 0
- TIA > 1, 7, 13 + Stroke > 2, 3
- Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17
We will also group as follows in AsymptSympt2G:
- Asymptomatic > 0
- TIA > 1, 7, 13 + Stroke > 2, 3 Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17
# Fix symptoms
attach(AEDB)
AEDB$sympt[is.na(AEDB$sympt)] <- -999
# Symptoms.5G
AEDB[,"Symptoms.5G"] <- NA
# AEDB$Symptoms.5G[sympt == "NA"] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == -999] <- NA
AEDB$Symptoms.5G[sympt == 0] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == 1 | sympt == 7 | sympt == 13] <- "TIA"
AEDB$Symptoms.5G[sympt == 2 | sympt == 3] <- "Stroke"
AEDB$Symptoms.5G[sympt == 4 | sympt == 14 | sympt == 15 ] <- "Ocular"
AEDB$Symptoms.5G[sympt == 8 | sympt == 11] <- "Retinal infarction"
AEDB$Symptoms.5G[sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Other"
# AsymptSympt
AEDB[,"AsymptSympt"] <- NA
AEDB$AsymptSympt[sympt == -999] <- NA
AEDB$AsymptSympt[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3] <- "Symptomatic"
AEDB$AsymptSympt[sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Ocular and others"
# AsymptSympt
AEDB[,"AsymptSympt2G"] <- NA
AEDB$AsymptSympt2G[sympt == -999] <- NA
AEDB$AsymptSympt2G[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt2G[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3 | sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Symptomatic"
detach(AEDB)
# table(AEDB$sympt, useNA = "ifany")
# table(AEDB$AsymptSympt2G, useNA = "ifany")
# table(AEDB$Symptoms.5G, useNA = "ifany")
#
# table(AEDB$AsymptSympt2G, AEDB$sympt, useNA = "ifany")
# table(AEDB$Symptoms.5G, AEDB$sympt, useNA = "ifany")
table(AEDB$AsymptSympt2G, AEDB$Symptoms.5G, useNA = "ifany")
Asymptomatic Ocular Other Retinal infarction Stroke TIA <NA>
Asymptomatic 333 0 0 0 0 0 0
Symptomatic 0 416 119 43 732 1045 0
<NA> 0 0 0 0 0 0 1103# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "sympt", "Symptoms.5G", "AsymptSympt"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# table(AEDB.temp$Symptoms.5G, AEDB.temp$AsymptSympt)
#
# rm(AEDB.temp)
We will also fix the plaquephenotypes variable.
Coding of symptoms is as follows:
- missing -999
- not relevant -888
- fibrous 1
- fibroatheromatous 2
- atheromatous 3
# Fix plaquephenotypes
attach(AEDB)
AEDB[,"OverallPlaquePhenotype"] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == 1] <- "fibrous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 2] <- "fibroatheromatous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 3] <- "atheromatous"
detach(AEDB)
table(AEDB$OverallPlaquePhenotype)
atheromatous fibroatheromatous fibrous
550 841 1439 # AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "plaquephenotype", "OverallPlaquePhenotype"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
We will also fix the diabetes status variable. We define diabetes as history of a diagnosis and/or use of glucose-lowering medications.
# Fix diabetes
attach(AEDB)
AEDB[,"DiabetesStatus"] <- NA
AEDB$DiabetesStatus[DM.composite == -999] <- NA
AEDB$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB)
table(AEDB$DM.composite)
0 1
2764 985 table(AEDB$DiabetesStatus)
Control (no Diabetes Dx/Med) Diabetes
2764 985 # AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
We will also fix the smoking status variable. We are interested in whether someone never, ever or is currently (at the time of inclusion) smoking. This is based on the questionnaire.
diet801: are you a smoker?diet802: did you smoke in the past?
We already have some variables indicating smoking status:
SmokingReported: patient has reported to smoke.SmokingYearOR: smoking in the year of surgery?SmokerCurrent: currently smoking?
require(labelled)
AEDB$diet801 <- to_factor(AEDB$diet801)
AEDB$diet802 <- to_factor(AEDB$diet802)
AEDB$diet805 <- to_factor(AEDB$diet805)
AEDB$SmokingReported <- to_factor(AEDB$SmokingReported)
AEDB$SmokerCurrent <- to_factor(AEDB$SmokerCurrent)
AEDB$SmokingYearOR <- to_factor(AEDB$SmokingYearOR)
# table(AEDB$diet801)
# table(AEDB$diet802)
# table(AEDB$SmokingReported)
# table(AEDB$SmokerCurrent)
# table(AEDB$SmokingYearOR)
# table(AEDB$SmokingReported, AEDB$SmokerCurrent, useNA = "ifany", dnn = c("Reported smoking", "Current smoker"))
#
# table(AEDB$diet801, AEDB$diet802, useNA = "ifany", dnn = c("Smoker", "Past smoker"))
cat("\nFixing smoking status.\n")
Fixing smoking status.attach(AEDB)
AEDB[,"SmokerStatus"] <- NA
AEDB$SmokerStatus[diet802 == "don't know"] <- "Never smoked"
AEDB$SmokerStatus[diet802 == "I still smoke"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "no"] <- "Never smoked"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "yes"] <- "Ex-smoker"
AEDB$SmokerStatus[SmokerCurrent == "yes"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no data available/missing"] <- NA
# AEDB$SmokerStatus[is.na(SmokerCurrent)] <- "Never smoked"
detach(AEDB)
cat("\n* Current smoking status.\n")
* Current smoking status.table(AEDB$SmokerCurrent,
useNA = "ifany",
dnn = c("Current smoker"))Current smoker
no data available/missing no yes <NA>
0 2364 1308 119 cat("\n* Updated smoking status.\n")
* Updated smoking status.table(AEDB$SmokerStatus,
useNA = "ifany",
dnn = c("Updated smoking status"))Updated smoking status
Current smoker Ex-smoker Never smoked <NA>
1308 1814 389 280 cat("\n* Comparing to 'SmokerCurrent'.\n")
* Comparing to 'SmokerCurrent'.table(AEDB$SmokerStatus, AEDB$SmokerCurrent,
useNA = "ifany",
dnn = c("Updated smoking status", "Current smoker")) Current smoker
Updated smoking status no data available/missing no yes <NA>
Current smoker 0 0 1308 0
Ex-smoker 0 1814 0 0
Never smoked 0 389 0 0
<NA> 0 161 0 119# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
We will also fix the alcohol status variable.
# Fix diabetes
attach(AEDB)
AEDB[,"AlcoholUse"] <- NA
AEDB$AlcoholUse[diet810 == -999] <- NA
AEDB$AlcoholUse[diet810 == 0] <- "No"
AEDB$AlcoholUse[diet810 == 1] <- "Yes"
detach(AEDB)
table(AEDB$AlcoholUse)
No Yes
1238 2345 # AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
We will also fix a history of CAD, stroke or peripheral intervention status variable. This will be based on CAD_history, Stroke_history, and Peripheral.interv
# Fix diabetes
attach(AEDB)
AEDB[,"MedHx_CVD"] <- NA
AEDB$MedHx_CVD[CAD_history == 0 | Stroke_history == 0 | Peripheral.interv == 0] <- "No"
AEDB$MedHx_CVD[CAD_history == 1 | Stroke_history == 1 | Peripheral.interv == 1] <- "yes"
detach(AEDB)
table(AEDB$CAD_history)
0 1
2430 1285 table(AEDB$Stroke_history)
0 1
2763 947 table(AEDB$Peripheral.interv)
0 1
2579 1099 table(AEDB$MedHx_CVD)
No yes
1309 2475 # AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
Athero-Express Biobank Study
Baseline characteristics
We are interested in the following variables at baseline.
- Age (years)
- Female sex (N, %)
- Hypertension (N, %)
- SBP (mmHg)
- DBP (mmHg)
- Diabetes mellitus (N, %)
- Total cholesterol levels (mg/dL)
- LDL cholesterol levels (mg/dL)
- HDL cholesterol levels (mg/dL)
- Triglyceride levels (mg/dL)
- Use of statins (N, %)
- Use of antiplatelet drugs (N, %)
- BMI (kg/m²)
- Smoking status (N, %)
- Never smokers
- Ex-smokers
- Current smokers
- History of CAD (N, %)
- History of PAD (N, %)
- Clinical manifestations
- Asymptomatic
- Amaurosis fugax
- TIA
- Stroke
- eGFR (mL/min/1.73 m²)
- MCP-1 plaque levels (pg/mL) (LUMINEX based, two experiments
MCP1, andMCP1_pg_ug_2015) - MCP-1 serum levels (pg/mL) (OLINK based)
cat("===========================================================================================\n")===========================================================================================cat("CREATE BASELINE TABLE\n")CREATE BASELINE TABLE# Baseline table variables
basetable_vars = c("Hospital", "ORyear",
"Age", "Gender",
"TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU",
"TC_final", "LDL_final", "HDL_final", "TG_final",
"hsCRP_plasma",
"systolic", "diastoli", "GFR_MDRD", "BMI",
"KDOQI", "BMI_WHO",
"SmokerStatus", "AlcoholUse",
"DiabetesStatus",
"Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
"Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
"Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
"restenos", "stenose",
"MedHx_CVD", "CAD_history", "PAOD", "Peripheral.interv",
"EP_composite", "EP_composite_time",
"macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
"neutrophils", "Mast_cells_plaque",
"IPH.bin", "vessel_density_averaged",
"Calc.bin", "Collagen.bin",
"Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
"IL6", "IL6_pg_ug_2015", "IL6R_pg_ug_2015",
"MCP1", "MCP1_pg_ug_2015")
basetable_bin = c("Gender",
"KDOQI", "BMI_WHO",
"SmokerStatus", "AlcoholUse",
"DiabetesStatus",
"Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
"Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
"Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
"restenos", "stenose",
"CAD_history", "PAOD", "Peripheral.interv",
"EP_composite", "Macrophages.bin", "SMC.bin",
"IPH.bin",
"Calc.bin", "Collagen.bin",
"Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype")
# basetable_bin
basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_conAll patients
Showing the baseline table of the whole Athero-Express Biobank.
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
# strata = "Symptoms.4g",
data = AEDB, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:3]
level Overall Missing
n 3791
Hospital % (freq) St. Antonius, Nieuwegein 45.8 (1735) 0.0
UMC Utrecht 54.2 (2056)
ORyear % (freq) No data available/missing 0.0 ( 0) 0.0
2002 2.5 ( 94)
2003 5.4 ( 204)
2004 7.6 ( 289)
2005 8.2 ( 309)
2006 7.5 ( 285)
2007 6.2 ( 234)
2008 5.9 ( 223)
2009 7.0 ( 267)
2010 8.1 ( 307)
2011 7.1 ( 269)
2012 8.2 ( 312)
2013 6.9 ( 262)
2014 7.9 ( 299)
2015 2.1 ( 79)
2016 3.3 ( 124)
2017 2.2 ( 85)
2018 2.1 ( 80)
2019 1.8 ( 69)
Age (mean (SD)) 68.907 (9.322) 0.0
Gender % (freq) female 30.6 (1160) 0.0
male 69.4 (2631)
TC_finalCU (mean (SD)) 185.220 (81.513) 46.8
LDL_finalCU (mean (SD)) 106.483 (40.683) 54.5
HDL_finalCU (mean (SD)) 46.593 (16.730) 51.1
TG_finalCU (mean (SD)) 154.233 (99.797) 51.8
TC_final (mean (SD)) 4.797 (2.111) 46.8
LDL_final (mean (SD)) 2.758 (1.054) 54.5
HDL_final (mean (SD)) 1.207 (0.433) 51.1
TG_final (mean (SD)) 1.743 (1.128) 51.8
hsCRP_plasma (mean (SD)) 19.250 (206.888) 60.7
systolic (mean (SD)) 150.907 (25.117) 13.5
diastoli (mean (SD)) 79.934 (21.853) 13.5
GFR_MDRD (mean (SD)) 74.849 (24.745) 6.5
BMI (mean (SD)) 26.336 (4.051) 7.5
KDOQI % (freq) No data available/missing 0.0 ( 0) 6.6
Normal kidney function 22.1 ( 839)
CKD 2 (Mild) 47.2 (1788)
CKD 3 (Moderate) 21.9 ( 830)
CKD 4 (Severe) 1.4 ( 53)
CKD 5 (Failure) 0.8 ( 32)
<NA> 6.6 ( 249)
BMI_WHO % (freq) No data available/missing 0.0 ( 0) 7.5
Underweight 1.2 ( 44)
Normal 35.2 (1335)
Overweight 42.0 (1594)
Obese 14.1 ( 533)
<NA> 7.5 ( 285)
SmokerStatus % (freq) Current smoker 34.5 (1308) 7.4
Ex-smoker 47.9 (1814)
Never smoked 10.3 ( 389)
<NA> 7.4 ( 280)
AlcoholUse % (freq) No 32.7 (1238) 5.5
Yes 61.9 (2345)
<NA> 5.5 ( 208)
DiabetesStatus % (freq) Control (no Diabetes Dx/Med) 72.9 (2764) 1.1
Diabetes 26.0 ( 985)
<NA> 1.1 ( 42)
Hypertension.selfreport % (freq) No data available/missing 0.0 ( 0) 4.0
no 23.7 ( 899)
yes 72.3 (2741)
<NA> 4.0 ( 151)
Hypertension.selfreportdrug % (freq) No data available/missing 0.0 ( 0) 5.5
no 28.6 (1085)
yes 65.9 (2499)
<NA> 5.5 ( 207)
Hypertension.composite % (freq) No data available/missing 0.0 ( 0) 1.3
no 13.3 ( 504)
yes 85.4 (3239)
<NA> 1.3 ( 48)
Hypertension.drugs % (freq) No data available/missing 0.0 ( 0) 1.5
no 21.0 ( 797)
yes 77.5 (2939)
<NA> 1.5 ( 55)
Med.anticoagulants % (freq) No data available/missing 0.0 ( 0) 1.6
no 85.6 (3246)
yes 12.8 ( 485)
<NA> 1.6 ( 60)
Med.all.antiplatelet % (freq) No data available/missing 0.0 ( 0) 1.6
no 13.7 ( 521)
yes 84.7 (3211)
<NA> 1.6 ( 59)
Med.Statin.LLD % (freq) No data available/missing 0.0 ( 0) 1.5
no 21.8 ( 826)
yes 76.7 (2909)
<NA> 1.5 ( 56)
Stroke_Dx % (freq) Missing 0.0 ( 0) 8.1
No stroke diagnosed 74.4 (2822)
Stroke diagnosed 17.5 ( 662)
<NA> 8.1 ( 307)
sympt % (freq) missing 29.1 (1103) 0.0
Asymptomatic 8.8 ( 333)
TIA 27.4 (1040)
minor stroke 12.1 ( 458)
Major stroke 7.2 ( 274)
Amaurosis fugax 10.5 ( 398)
Four vessel disease 1.1 ( 43)
Vertebrobasilary TIA 0.1 ( 5)
Retinal infarction 1.0 ( 37)
Symptomatic, but aspecific symtoms 1.6 ( 61)
Contralateral symptomatic occlusion 0.3 ( 12)
retinal infarction 0.2 ( 6)
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0 ( 1)
retinal infarction + TIAs 0.0 ( 0)
Ocular ischemic syndrome 0.5 ( 18)
ischemisch glaucoom 0.0 ( 0)
subclavian steal syndrome 0.1 ( 2)
TGA 0.0 ( 0)
Symptoms.5G % (freq) Asymptomatic 8.8 ( 333) 29.1
Ocular 11.0 ( 416)
Other 3.1 ( 119)
Retinal infarction 1.1 ( 43)
Stroke 19.3 ( 732)
TIA 27.6 (1045)
<NA> 29.1 (1103)
AsymptSympt % (freq) Asymptomatic 8.8 ( 333) 29.1
Ocular and others 15.2 ( 578)
Symptomatic 46.9 (1777)
<NA> 29.1 (1103)
AsymptSympt2G % (freq) Asymptomatic 8.8 ( 333) 29.1
Symptomatic 62.1 (2355)
<NA> 29.1 (1103)
restenos % (freq) missing 0.0 ( 0) 4.0
de novo 87.0 (3297)
restenosis 8.8 ( 334)
stenose bij angioseal na PTCA 0.2 ( 7)
<NA> 4.0 ( 153)
stenose % (freq) missing 0.0 ( 0) 7.0
0-49% 0.7 ( 25)
50-70% 6.8 ( 256)
70-90% 35.6 (1349)
90-99% 29.9 (1132)
100% (Occlusion) 14.8 ( 560)
NA 0.1 ( 3)
50-99% 2.6 ( 99)
70-99% 2.6 ( 100)
99 0.1 ( 2)
<NA> 7.0 ( 265)
MedHx_CVD % (freq) No 34.5 (1309) 0.2
yes 65.3 (2475)
<NA> 0.2 ( 7)
CAD_history % (freq) Missing 0.0 ( 0) 2.0
No history CAD 64.1 (2430)
History CAD 33.9 (1285)
<NA> 2.0 ( 76)
PAOD % (freq) missing/no data 0.0 ( 0) 1.6
no 55.1 (2088)
yes 43.4 (1644)
<NA> 1.6 ( 59)
Peripheral.interv % (freq) no 68.0 (2579) 3.0
yes 29.0 (1099)
<NA> 3.0 ( 113)
EP_composite % (freq) No data available. 0.0 ( 0) 7.3
No composite endpoints 60.6 (2297)
Composite endpoints 32.1 (1218)
<NA> 7.3 ( 276)
EP_composite_time (mean (SD)) 2.266 (1.203) 7.4
macmean0 (mean (SD)) 0.656 (1.154) 32.4
smcmean0 (mean (SD)) 2.291 (6.620) 32.4
Macrophages.bin % (freq) no/minor 42.3 (1602) 25.7
moderate/heavy 32.0 (1215)
<NA> 25.7 ( 974)
SMC.bin % (freq) no/minor 22.9 ( 870) 25.3
moderate/heavy 51.8 (1962)
<NA> 25.3 ( 959)
neutrophils (mean (SD)) 162.985 (490.469) 91.0
Mast_cells_plaque (mean (SD)) 165.663 (163.421) 93.0
IPH.bin % (freq) no 32.3 (1223) 24.8
yes 42.9 (1628)
<NA> 24.8 ( 940)
vessel_density_averaged (mean (SD)) 8.030 (6.348) 48.0
Calc.bin % (freq) no/minor 37.9 (1437) 24.7
moderate/heavy 37.4 (1416)
<NA> 24.7 ( 938)
Collagen.bin % (freq) no/minor 14.2 ( 540) 25.2
moderate/heavy 60.6 (2297)
<NA> 25.2 ( 954)
Fat.bin_10 % (freq) <10% 32.3 (1226) 24.7
>10% 42.9 (1628)
<NA> 24.7 ( 937)
Fat.bin_40 % (freq) <40% 60.0 (2274) 24.7
>40% 15.3 ( 580)
<NA> 24.7 ( 937)
OverallPlaquePhenotype % (freq) atheromatous 14.5 ( 550) 25.3
fibroatheromatous 22.2 ( 841)
fibrous 38.0 (1439)
<NA> 25.3 ( 961)
IL6 (mean (SD)) 94.451 (278.490) 84.5
IL6_pg_ug_2015 (mean (SD)) 0.135 (0.541) 67.2
IL6R_pg_ug_2015 (mean (SD)) 0.212 (0.251) 67.1
MCP1 (mean (SD)) 130.926 (118.422) 83.7
MCP1_pg_ug_2015 (mean (SD)) 0.596 (0.880) 65.5 CEA patients
Showing the baseline table of the CEA patients in the Athero-Express Biobank.
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.CEA.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
# strata = "Symptoms.4g",
data = AEDB.CEA, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:3]
level Overall Missing
n 2421
Hospital % (freq) St. Antonius, Nieuwegein 39.2 ( 948) 0.0
UMC Utrecht 60.8 (1473)
ORyear % (freq) No data available/missing 0.0 ( 0) 0.0
2002 3.3 ( 81)
2003 6.5 ( 157)
2004 7.8 ( 190)
2005 7.6 ( 185)
2006 7.6 ( 183)
2007 6.3 ( 152)
2008 5.7 ( 138)
2009 7.5 ( 181)
2010 6.6 ( 159)
2011 6.7 ( 163)
2012 7.3 ( 176)
2013 6.2 ( 149)
2014 6.7 ( 163)
2015 3.1 ( 76)
2016 3.5 ( 85)
2017 2.7 ( 65)
2018 2.7 ( 66)
2019 2.1 ( 52)
Age (mean (SD)) 69.105 (9.302) 0.0
Gender % (freq) female 30.5 ( 738) 0.0
male 69.5 (1683)
TC_finalCU (mean (SD)) 184.803 (56.262) 38.0
LDL_finalCU (mean (SD)) 108.420 (41.744) 45.6
HDL_finalCU (mean (SD)) 46.435 (17.005) 41.7
TG_finalCU (mean (SD)) 151.216 (91.277) 42.8
TC_final (mean (SD)) 4.786 (1.457) 38.0
LDL_final (mean (SD)) 2.808 (1.081) 45.6
HDL_final (mean (SD)) 1.203 (0.440) 41.7
TG_final (mean (SD)) 1.709 (1.031) 42.8
hsCRP_plasma (mean (SD)) 19.914 (231.655) 53.0
systolic (mean (SD)) 152.419 (25.166) 11.3
diastoli (mean (SD)) 81.318 (25.188) 11.3
GFR_MDRD (mean (SD)) 73.121 (21.152) 5.4
BMI (mean (SD)) 26.488 (3.977) 5.9
KDOQI % (freq) No data available/missing 0.0 ( 0) 5.5
Normal kidney function 19.1 ( 462)
CKD 2 (Mild) 50.9 (1232)
CKD 3 (Moderate) 22.8 ( 553)
CKD 4 (Severe) 1.3 ( 32)
CKD 5 (Failure) 0.4 ( 10)
<NA> 5.5 ( 132)
BMI_WHO % (freq) No data available/missing 0.0 ( 0) 5.9
Underweight 1.0 ( 24)
Normal 35.1 ( 850)
Overweight 43.4 (1051)
Obese 14.5 ( 352)
<NA> 5.9 ( 144)
SmokerStatus % (freq) Current smoker 33.2 ( 803) 5.9
Ex-smoker 48.0 (1163)
Never smoked 12.9 ( 313)
<NA> 5.9 ( 142)
AlcoholUse % (freq) No 34.5 ( 835) 4.0
Yes 61.5 (1488)
<NA> 4.0 ( 98)
DiabetesStatus % (freq) Control (no Diabetes Dx/Med) 75.2 (1820) 1.1
Diabetes 23.7 ( 574)
<NA> 1.1 ( 27)
Hypertension.selfreport % (freq) No data available/missing 0.0 ( 0) 3.2
no 24.3 ( 589)
yes 72.4 (1754)
<NA> 3.2 ( 78)
Hypertension.selfreportdrug % (freq) No data available/missing 0.0 ( 0) 4.4
no 29.9 ( 725)
yes 65.6 (1589)
<NA> 4.4 ( 107)
Hypertension.composite % (freq) No data available/missing 0.0 ( 0) 1.2
no 14.6 ( 353)
yes 84.3 (2040)
<NA> 1.2 ( 28)
Hypertension.drugs % (freq) No data available/missing 0.0 ( 0) 1.4
no 23.3 ( 565)
yes 75.3 (1823)
<NA> 1.4 ( 33)
Med.anticoagulants % (freq) No data available/missing 0.0 ( 0) 1.6
no 87.3 (2114)
yes 11.1 ( 269)
<NA> 1.6 ( 38)
Med.all.antiplatelet % (freq) No data available/missing 0.0 ( 0) 1.5
no 12.2 ( 295)
yes 86.3 (2090)
<NA> 1.5 ( 36)
Med.Statin.LLD % (freq) No data available/missing 0.0 ( 0) 1.4
no 20.3 ( 491)
yes 78.3 (1896)
<NA> 1.4 ( 34)
Stroke_Dx % (freq) Missing 0.0 ( 0) 6.9
No stroke diagnosed 71.5 (1731)
Stroke diagnosed 21.6 ( 524)
<NA> 6.9 ( 166)
sympt % (freq) missing 0.0 ( 0) 0.0
Asymptomatic 11.2 ( 270)
TIA 39.7 ( 961)
minor stroke 16.8 ( 407)
Major stroke 9.8 ( 238)
Amaurosis fugax 15.7 ( 379)
Four vessel disease 1.6 ( 38)
Vertebrobasilary TIA 0.2 ( 5)
Retinal infarction 1.4 ( 34)
Symptomatic, but aspecific symtoms 2.2 ( 53)
Contralateral symptomatic occlusion 0.5 ( 11)
retinal infarction 0.2 ( 6)
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0 ( 1)
retinal infarction + TIAs 0.0 ( 0)
Ocular ischemic syndrome 0.7 ( 16)
ischemisch glaucoom 0.0 ( 0)
subclavian steal syndrome 0.1 ( 2)
TGA 0.0 ( 0)
Symptoms.5G % (freq) Asymptomatic 11.2 ( 270) 0.0
Ocular 16.3 ( 395)
Other 4.3 ( 105)
Retinal infarction 1.7 ( 40)
Stroke 26.6 ( 645)
TIA 39.9 ( 966)
AsymptSympt % (freq) Asymptomatic 11.2 ( 270) 0.0
Ocular and others 22.3 ( 540)
Symptomatic 66.5 (1611)
AsymptSympt2G % (freq) Asymptomatic 11.2 ( 270) 0.0
Symptomatic 88.8 (2151)
restenos % (freq) missing 0.0 ( 0) 1.4
de novo 93.7 (2268)
restenosis 4.9 ( 118)
stenose bij angioseal na PTCA 0.0 ( 0)
<NA> 1.4 ( 35)
stenose % (freq) missing 0.0 ( 0) 2.0
0-49% 0.5 ( 13)
50-70% 7.8 ( 189)
70-90% 46.6 (1127)
90-99% 38.3 ( 927)
100% (Occlusion) 1.3 ( 31)
NA 0.0 ( 1)
50-99% 0.6 ( 15)
70-99% 2.8 ( 68)
99 0.1 ( 2)
<NA> 2.0 ( 48)
MedHx_CVD % (freq) No 36.8 ( 892) 0.0
yes 63.2 (1529)
CAD_history % (freq) Missing 0.0 ( 0) 1.9
No history CAD 66.8 (1618)
History CAD 31.2 ( 756)
<NA> 1.9 ( 47)
PAOD % (freq) missing/no data 0.0 ( 0) 2.0
no 77.5 (1876)
yes 20.5 ( 497)
<NA> 2.0 ( 48)
Peripheral.interv % (freq) no 77.2 (1868) 2.9
yes 19.9 ( 482)
<NA> 2.9 ( 71)
EP_composite % (freq) No data available. 0.0 ( 0) 5.0
No composite endpoints 70.6 (1709)
Composite endpoints 24.4 ( 590)
<NA> 5.0 ( 122)
EP_composite_time (mean (SD)) 2.479 (1.109) 5.2
macmean0 (mean (SD)) 0.768 (1.184) 29.7
smcmean0 (mean (SD)) 1.985 (2.381) 29.9
Macrophages.bin % (freq) no/minor 34.9 ( 846) 24.1
moderate/heavy 40.9 ( 991)
<NA> 24.1 ( 584)
SMC.bin % (freq) no/minor 24.9 ( 602) 23.8
moderate/heavy 51.3 (1242)
<NA> 23.8 ( 577)
neutrophils (mean (SD)) 147.151 (419.998) 87.4
Mast_cells_plaque (mean (SD)) 164.488 (163.771) 90.0
IPH.bin % (freq) no 30.7 ( 744) 23.5
yes 45.8 (1108)
<NA> 23.5 ( 569)
vessel_density_averaged (mean (SD)) 8.318 (6.388) 35.1
Calc.bin % (freq) no/minor 41.6 (1006) 23.4
moderate/heavy 35.1 ( 849)
<NA> 23.4 ( 566)
Collagen.bin % (freq) no/minor 15.8 ( 382) 23.6
moderate/heavy 60.6 (1467)
<NA> 23.6 ( 572)
Fat.bin_10 % (freq) <10% 22.4 ( 542) 23.3
>10% 54.3 (1314)
<NA> 23.3 ( 565)
Fat.bin_40 % (freq) <40% 56.2 (1360) 23.3
>40% 20.5 ( 496)
<NA> 23.3 ( 565)
OverallPlaquePhenotype % (freq) atheromatous 19.8 ( 480) 23.7
fibroatheromatous 27.8 ( 672)
fibrous 28.7 ( 695)
<NA> 23.7 ( 574)
IL6 (mean (SD)) 98.812 (292.457) 78.2
IL6_pg_ug_2015 (mean (SD)) 0.138 (0.556) 52.5
IL6R_pg_ug_2015 (mean (SD)) 0.212 (0.251) 52.4
MCP1 (mean (SD)) 135.763 (120.028) 76.7
MCP1_pg_ug_2015 (mean (SD)) 0.612 (0.905) 50.6 CEA patients with MCP1_pg_ug_2015
Showing the baseline table of the CEA patients in the Athero-Express Biobank with MCP1_pg_ug_2015.
AEDB.CEA.subset <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015))
AEDB.CEA.subset.AsymptSympt.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
strata = "AsymptSympt2G",
data = AEDB.CEA.subset, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:6] Stratified by AsymptSympt2G
level Asymptomatic Symptomatic p test Missing
n 131 1065
Hospital % (freq) St. Antonius, Nieuwegein 50.4 ( 66) 46.6 ( 496) 0.464 0.0
UMC Utrecht 49.6 ( 65) 53.4 ( 569)
ORyear % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
2002 10.7 ( 14) 3.9 ( 42)
2003 7.6 ( 10) 9.4 ( 100)
2004 17.6 ( 23) 11.5 ( 123)
2005 9.9 ( 13) 11.2 ( 119)
2006 10.7 ( 14) 10.2 ( 109)
2007 11.5 ( 15) 10.5 ( 112)
2008 7.6 ( 10) 7.4 ( 79)
2009 7.6 ( 10) 8.4 ( 89)
2010 5.3 ( 7) 7.6 ( 81)
2011 6.1 ( 8) 9.5 ( 101)
2012 5.3 ( 7) 8.3 ( 88)
2013 0.0 ( 0) 2.0 ( 21)
2014 0.0 ( 0) 0.1 ( 1)
2015 0.0 ( 0) 0.0 ( 0)
2016 0.0 ( 0) 0.0 ( 0)
2017 0.0 ( 0) 0.0 ( 0)
2018 0.0 ( 0) 0.0 ( 0)
2019 0.0 ( 0) 0.0 ( 0)
Age (mean (SD)) 66.237 (9.184) 68.941 (9.119) 0.001 0.0
Gender % (freq) female 23.7 ( 31) 31.4 ( 334) 0.088 0.0
male 76.3 (100) 68.6 ( 731)
TC_finalCU (mean (SD)) 175.987 (47.184) 183.420 (48.377) 0.180 33.4
LDL_finalCU (mean (SD)) 102.781 (38.324) 109.247 (41.008) 0.191 39.6
HDL_finalCU (mean (SD)) 43.701 (14.754) 45.814 (18.526) 0.317 36.4
TG_finalCU (mean (SD)) 157.650 (89.246) 145.238 (84.872) 0.211 36.0
TC_final (mean (SD)) 4.558 (1.222) 4.751 (1.253) 0.180 33.4
LDL_final (mean (SD)) 2.662 (0.993) 2.829 (1.062) 0.191 39.6
HDL_final (mean (SD)) 1.132 (0.382) 1.187 (0.480) 0.317 36.4
TG_final (mean (SD)) 1.781 (1.008) 1.641 (0.959) 0.211 36.0
hsCRP_plasma (mean (SD)) 5.688 (19.440) 16.588 (113.882) 0.379 38.8
systolic (mean (SD)) 153.577 (24.327) 155.822 (26.180) 0.390 14.0
diastoli (mean (SD)) 80.622 (13.225) 82.894 (13.581) 0.095 14.0
GFR_MDRD (mean (SD)) 71.026 (20.424) 71.879 (20.071) 0.653 3.5
BMI (mean (SD)) 26.623 (3.391) 26.321 (3.748) 0.383 4.2
KDOQI % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 3.6
Normal kidney function 17.6 ( 23) 17.3 ( 184)
CKD 2 (Mild) 49.6 ( 65) 53.2 ( 567)
CKD 3 (Moderate) 28.2 ( 37) 24.3 ( 259)
CKD 4 (Severe) 0.0 ( 0) 1.2 ( 13)
CKD 5 (Failure) 0.8 ( 1) 0.4 ( 4)
<NA> 3.8 ( 5) 3.6 ( 38)
BMI_WHO % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 4.3
Underweight 0.8 ( 1) 0.9 ( 10)
Normal 32.8 ( 43) 35.6 ( 379)
Overweight 51.1 ( 67) 46.1 ( 491)
Obese 13.0 ( 17) 12.8 ( 136)
<NA> 2.3 ( 3) 4.6 ( 49)
SmokerStatus % (freq) Current smoker 30.5 ( 40) 36.2 ( 385) 0.077 3.8
Ex-smoker 57.3 ( 75) 45.6 ( 486)
Never smoked 9.9 ( 13) 14.3 ( 152)
<NA> 2.3 ( 3) 3.9 ( 42)
AlcoholUse % (freq) No 38.2 ( 50) 33.3 ( 355) 0.359 4.0
Yes 59.5 ( 78) 62.4 ( 665)
<NA> 2.3 ( 3) 4.2 ( 45)
DiabetesStatus % (freq) Control (no Diabetes Dx/Med) 76.3 (100) 77.4 ( 824) 0.876 0.0
Diabetes 23.7 ( 31) 22.6 ( 241)
Hypertension.selfreport % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 2.0
no 23.7 ( 31) 26.6 ( 283)
yes 75.6 ( 99) 71.3 ( 759)
<NA> 0.8 ( 1) 2.2 ( 23)
Hypertension.selfreportdrug % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 2.7
no 30.5 ( 40) 32.9 ( 350)
yes 67.9 ( 89) 64.3 ( 685)
<NA> 1.5 ( 2) 2.8 ( 30)
Hypertension.composite % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
no 9.9 ( 13) 14.3 ( 152)
yes 90.1 (118) 85.7 ( 913)
Hypertension.drugs % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.2
no 14.5 ( 19) 23.3 ( 248)
yes 85.5 (112) 76.5 ( 815)
<NA> 0.0 ( 0) 0.2 ( 2)
Med.anticoagulants % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.2
no 89.3 (117) 88.0 ( 937)
yes 10.7 ( 14) 11.8 ( 126)
<NA> 0.0 ( 0) 0.2 ( 2)
Med.all.antiplatelet % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.4
no 6.1 ( 8) 11.0 ( 117)
yes 93.1 (122) 88.6 ( 944)
<NA> 0.8 ( 1) 0.4 ( 4)
Med.Statin.LLD % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.2
no 15.3 ( 20) 22.7 ( 242)
yes 84.7 (111) 77.1 ( 821)
<NA> 0.0 ( 0) 0.2 ( 2)
Stroke_Dx % (freq) Missing 0.0 ( 0) 0.0 ( 0) NaN 5.4
No stroke diagnosed 80.2 (105) 75.2 ( 801)
Stroke diagnosed 14.5 ( 19) 19.4 ( 207)
<NA> 5.3 ( 7) 5.4 ( 57)
sympt % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
Asymptomatic 100.0 (131) 0.0 ( 0)
TIA 0.0 ( 0) 46.4 ( 494)
minor stroke 0.0 ( 0) 16.7 ( 178)
Major stroke 0.0 ( 0) 12.2 ( 130)
Amaurosis fugax 0.0 ( 0) 17.2 ( 183)
Four vessel disease 0.0 ( 0) 2.2 ( 23)
Vertebrobasilary TIA 0.0 ( 0) 0.2 ( 2)
Retinal infarction 0.0 ( 0) 1.4 ( 15)
Symptomatic, but aspecific symtoms 0.0 ( 0) 2.7 ( 29)
Contralateral symptomatic occlusion 0.0 ( 0) 0.6 ( 6)
retinal infarction 0.0 ( 0) 0.3 ( 3)
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0 ( 0) 0.1 ( 1)
retinal infarction + TIAs 0.0 ( 0) 0.0 ( 0)
Ocular ischemic syndrome 0.0 ( 0) 0.1 ( 1)
ischemisch glaucoom 0.0 ( 0) 0.0 ( 0)
subclavian steal syndrome 0.0 ( 0) 0.0 ( 0)
TGA 0.0 ( 0) 0.0 ( 0)
Symptoms.5G % (freq) Asymptomatic 100.0 (131) 0.0 ( 0) <0.001 0.0
Ocular 0.0 ( 0) 17.3 ( 184)
Other 0.0 ( 0) 5.5 ( 59)
Retinal infarction 0.0 ( 0) 1.7 ( 18)
Stroke 0.0 ( 0) 28.9 ( 308)
TIA 0.0 ( 0) 46.6 ( 496)
AsymptSympt % (freq) Asymptomatic 100.0 (131) 0.0 ( 0) <0.001 0.0
Ocular and others 0.0 ( 0) 24.5 ( 261)
Symptomatic 0.0 ( 0) 75.5 ( 804)
AsymptSympt2G % (freq) Asymptomatic 100.0 (131) 0.0 ( 0) <0.001 0.0
Symptomatic 0.0 ( 0) 100.0 (1065)
restenos % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN 2.3
de novo 93.9 (123) 94.7 (1009)
restenosis 2.3 ( 3) 3.2 ( 34)
stenose bij angioseal na PTCA 0.0 ( 0) 0.0 ( 0)
<NA> 3.8 ( 5) 2.1 ( 22)
stenose % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN 3.2
0-49% 0.0 ( 0) 0.6 ( 6)
50-70% 3.1 ( 4) 6.4 ( 68)
70-90% 51.1 ( 67) 44.6 ( 475)
90-99% 41.2 ( 54) 42.7 ( 455)
100% (Occlusion) 0.0 ( 0) 0.9 ( 10)
NA 0.0 ( 0) 0.0 ( 0)
50-99% 0.8 ( 1) 0.4 ( 4)
70-99% 0.0 ( 0) 1.3 ( 14)
99 0.0 ( 0) 0.0 ( 0)
<NA> 3.8 ( 5) 3.1 ( 33)
MedHx_CVD % (freq) No 38.9 ( 51) 36.9 ( 393) 0.720 0.0
yes 61.1 ( 80) 63.1 ( 672)
CAD_history % (freq) Missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
No history CAD 61.8 ( 81) 69.9 ( 744)
History CAD 38.2 ( 50) 30.1 ( 321)
PAOD % (freq) missing/no data 0.0 ( 0) 0.0 ( 0) NaN 0.0
no 74.0 ( 97) 79.5 ( 847)
yes 26.0 ( 34) 20.5 ( 218)
Peripheral.interv % (freq) no 74.0 ( 97) 82.5 ( 879) 0.042 0.3
yes 26.0 ( 34) 17.2 ( 183)
<NA> 0.0 ( 0) 0.3 ( 3)
EP_composite % (freq) No data available. 0.0 ( 0) 0.0 ( 0) NaN 0.8
No composite endpoints 67.2 ( 88) 74.3 ( 791)
Composite endpoints 32.8 ( 43) 24.9 ( 265)
<NA> 0.0 ( 0) 0.8 ( 9)
EP_composite_time (mean (SD)) 2.614 (0.931) 2.613 (1.095) 0.992 0.9
macmean0 (mean (SD)) 0.837 (1.088) 0.781 (1.231) 0.623 2.3
smcmean0 (mean (SD)) 2.152 (1.861) 1.904 (2.222) 0.223 2.7
Macrophages.bin % (freq) no/minor 48.9 ( 64) 47.4 ( 505) 0.583 1.9
moderate/heavy 50.4 ( 66) 50.5 ( 538)
<NA> 0.8 ( 1) 2.1 ( 22)
SMC.bin % (freq) no/minor 22.9 ( 30) 32.2 ( 343) 0.085 1.8
moderate/heavy 75.6 ( 99) 65.9 ( 702)
<NA> 1.5 ( 2) 1.9 ( 20)
neutrophils (mean (SD)) 157.643 (507.380) 172.872 (477.038) 0.876 81.9
Mast_cells_plaque (mean (SD)) 111.400 (112.037) 183.284 (180.156) 0.056 86.1
IPH.bin % (freq) no 41.2 ( 54) 37.9 ( 404) 0.561 1.7
yes 58.0 ( 76) 60.3 ( 642)
<NA> 0.8 ( 1) 1.8 ( 19)
vessel_density_averaged (mean (SD)) 8.608 (6.547) 8.408 (6.469) 0.750 8.7
[ reached getOption("max.print") -- omitted 21 rows ]CEA patients with MCP1_pg_ug_2015 and MCP1
Showing the baseline table of the CEA patients in the Athero-Express Biobank with MCP1_pg_ug_2015 and MCP1.
AEDB.CEA.subset.combo <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015) | !is.na(MCP1))
AEDB.CEA.subset.combo.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
strata = "AsymptSympt2G",
data = AEDB.CEA.subset.combo, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:6] Stratified by AsymptSympt2G
level Asymptomatic Symptomatic p test Missing
n 161 1165
Hospital % (freq) St. Antonius, Nieuwegein 52.2 ( 84) 47.0 ( 547) 0.246 0.0
UMC Utrecht 47.8 ( 77) 53.0 ( 618)
ORyear % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
2002 10.6 ( 17) 4.8 ( 56)
2003 11.8 ( 19) 10.6 ( 124)
2004 19.9 ( 32) 12.2 ( 142)
2005 13.7 ( 22) 13.3 ( 155)
2006 8.7 ( 14) 10.0 ( 116)
2007 9.3 ( 15) 9.6 ( 112)
2008 6.2 ( 10) 6.8 ( 79)
2009 6.2 ( 10) 7.6 ( 89)
2010 4.3 ( 7) 7.0 ( 81)
2011 5.0 ( 8) 8.7 ( 101)
2012 4.3 ( 7) 7.6 ( 88)
2013 0.0 ( 0) 1.8 ( 21)
2014 0.0 ( 0) 0.1 ( 1)
2015 0.0 ( 0) 0.0 ( 0)
2016 0.0 ( 0) 0.0 ( 0)
2017 0.0 ( 0) 0.0 ( 0)
2018 0.0 ( 0) 0.0 ( 0)
2019 0.0 ( 0) 0.0 ( 0)
Age (mean (SD)) 65.901 (9.051) 68.788 (9.081) <0.001 0.0
Gender % (freq) female 23.0 ( 37) 30.4 ( 354) 0.066 0.0
male 77.0 (124) 69.6 ( 811)
TC_finalCU (mean (SD)) 179.199 (45.274) 183.983 (48.290) 0.331 32.7
LDL_finalCU (mean (SD)) 104.132 (37.590) 109.642 (41.227) 0.215 39.8
HDL_finalCU (mean (SD)) 44.749 (14.890) 45.808 (18.231) 0.568 36.1
TG_finalCU (mean (SD)) 158.699 (87.584) 145.942 (83.223) 0.143 35.6
TC_final (mean (SD)) 4.641 (1.173) 4.765 (1.251) 0.331 32.7
LDL_final (mean (SD)) 2.697 (0.974) 2.840 (1.068) 0.215 39.8
HDL_final (mean (SD)) 1.159 (0.386) 1.186 (0.472) 0.568 36.1
TG_final (mean (SD)) 1.793 (0.990) 1.649 (0.940) 0.143 35.6
hsCRP_plasma (mean (SD)) 6.846 (21.838) 16.213 (110.899) 0.393 40.6
systolic (mean (SD)) 152.838 (24.600) 155.742 (26.411) 0.225 13.5
diastoli (mean (SD)) 80.824 (12.855) 82.873 (13.549) 0.096 13.5
GFR_MDRD (mean (SD)) 70.440 (19.793) 71.901 (20.142) 0.396 3.5
BMI (mean (SD)) 26.626 (3.572) 26.350 (3.768) 0.389 4.4
KDOQI % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 3.5
Normal kidney function 14.9 ( 24) 17.4 ( 203)
CKD 2 (Mild) 50.9 ( 82) 53.4 ( 622)
CKD 3 (Moderate) 29.8 ( 48) 23.9 ( 279)
CKD 4 (Severe) 0.0 ( 0) 1.3 ( 15)
CKD 5 (Failure) 0.6 ( 1) 0.4 ( 5)
<NA> 3.7 ( 6) 3.5 ( 41)
BMI_WHO % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 4.6
Underweight 1.2 ( 2) 0.9 ( 11)
Normal 32.3 ( 52) 35.5 ( 414)
Overweight 49.7 ( 80) 45.6 ( 531)
Obese 13.7 ( 22) 13.1 ( 153)
<NA> 3.1 ( 5) 4.8 ( 56)
SmokerStatus % (freq) Current smoker 29.2 ( 47) 36.0 ( 419) 0.072 4.0
Ex-smoker 56.5 ( 91) 45.7 ( 532)
Never smoked 11.8 ( 19) 14.2 ( 165)
<NA> 2.5 ( 4) 4.2 ( 49)
AlcoholUse % (freq) No 38.5 ( 62) 33.6 ( 392) 0.223 3.8
Yes 59.6 ( 96) 62.2 ( 725)
<NA> 1.9 ( 3) 4.1 ( 48)
DiabetesStatus % (freq) Control (no Diabetes Dx/Med) 78.3 (126) 77.3 ( 900) 0.852 0.0
Diabetes 21.7 ( 35) 22.7 ( 265)
Hypertension.selfreport % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 1.9
no 25.5 ( 41) 26.5 ( 309)
yes 73.9 (119) 71.4 ( 832)
<NA> 0.6 ( 1) 2.1 ( 24)
Hypertension.selfreportdrug % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 2.4
no 32.3 ( 52) 32.9 ( 383)
yes 66.5 (107) 64.5 ( 752)
<NA> 1.2 ( 2) 2.6 ( 30)
Hypertension.composite % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
no 11.2 ( 18) 14.1 ( 164)
yes 88.8 (143) 85.9 (1001)
Hypertension.drugs % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.2
no 15.5 ( 25) 22.7 ( 265)
yes 83.9 (135) 77.1 ( 898)
<NA> 0.6 ( 1) 0.2 ( 2)
Med.anticoagulants % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.2
no 89.4 (144) 88.0 (1025)
yes 9.9 ( 16) 11.8 ( 138)
<NA> 0.6 ( 1) 0.2 ( 2)
Med.all.antiplatelet % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.5
no 6.2 ( 10) 10.8 ( 126)
yes 92.5 (149) 88.8 (1035)
<NA> 1.2 ( 2) 0.3 ( 4)
Med.Statin.LLD % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.2
no 17.4 ( 28) 23.2 ( 270)
yes 82.0 (132) 76.7 ( 893)
<NA> 0.6 ( 1) 0.2 ( 2)
Stroke_Dx % (freq) Missing 0.0 ( 0) 0.0 ( 0) NaN 5.5
No stroke diagnosed 80.1 (129) 75.5 ( 880)
Stroke diagnosed 13.7 ( 22) 19.1 ( 222)
<NA> 6.2 ( 10) 5.4 ( 63)
sympt % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
Asymptomatic 100.0 (161) 0.0 ( 0)
TIA 0.0 ( 0) 46.6 ( 543)
minor stroke 0.0 ( 0) 17.2 ( 200)
Major stroke 0.0 ( 0) 11.6 ( 135)
Amaurosis fugax 0.0 ( 0) 16.9 ( 197)
Four vessel disease 0.0 ( 0) 2.1 ( 25)
Vertebrobasilary TIA 0.0 ( 0) 0.2 ( 2)
Retinal infarction 0.0 ( 0) 1.4 ( 16)
Symptomatic, but aspecific symtoms 0.0 ( 0) 3.1 ( 36)
Contralateral symptomatic occlusion 0.0 ( 0) 0.5 ( 6)
retinal infarction 0.0 ( 0) 0.3 ( 3)
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0 ( 0) 0.1 ( 1)
retinal infarction + TIAs 0.0 ( 0) 0.0 ( 0)
Ocular ischemic syndrome 0.0 ( 0) 0.1 ( 1)
ischemisch glaucoom 0.0 ( 0) 0.0 ( 0)
subclavian steal syndrome 0.0 ( 0) 0.0 ( 0)
TGA 0.0 ( 0) 0.0 ( 0)
Symptoms.5G % (freq) Asymptomatic 100.0 (161) 0.0 ( 0) <0.001 0.0
Ocular 0.0 ( 0) 17.0 ( 198)
Other 0.0 ( 0) 5.8 ( 68)
Retinal infarction 0.0 ( 0) 1.6 ( 19)
Stroke 0.0 ( 0) 28.8 ( 335)
TIA 0.0 ( 0) 46.8 ( 545)
AsymptSympt % (freq) Asymptomatic 100.0 (161) 0.0 ( 0) <0.001 0.0
Ocular and others 0.0 ( 0) 24.5 ( 285)
Symptomatic 0.0 ( 0) 75.5 ( 880)
AsymptSympt2G % (freq) Asymptomatic 100.0 (161) 0.0 ( 0) <0.001 0.0
Symptomatic 0.0 ( 0) 100.0 (1165)
restenos % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN 2.0
de novo 93.2 (150) 95.0 (1107)
restenosis 3.7 ( 6) 3.1 ( 36)
stenose bij angioseal na PTCA 0.0 ( 0) 0.0 ( 0)
<NA> 3.1 ( 5) 1.9 ( 22)
stenose % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN 2.9
0-49% 0.0 ( 0) 0.6 ( 7)
50-70% 2.5 ( 4) 6.2 ( 72)
70-90% 50.9 ( 82) 44.6 ( 520)
90-99% 42.9 ( 69) 43.3 ( 504)
100% (Occlusion) 0.0 ( 0) 0.9 ( 11)
NA 0.0 ( 0) 0.0 ( 0)
50-99% 0.6 ( 1) 0.3 ( 4)
70-99% 0.0 ( 0) 1.2 ( 14)
99 0.0 ( 0) 0.0 ( 0)
<NA> 3.1 ( 5) 2.8 ( 33)
MedHx_CVD % (freq) No 37.3 ( 60) 36.7 ( 428) 0.965 0.0
yes 62.7 (101) 63.3 ( 737)
CAD_history % (freq) Missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
No history CAD 59.0 ( 95) 69.1 ( 805)
History CAD 41.0 ( 66) 30.9 ( 360)
PAOD % (freq) missing/no data 0.0 ( 0) 0.0 ( 0) NaN 0.0
no 73.9 (119) 79.8 ( 930)
yes 26.1 ( 42) 20.2 ( 235)
Peripheral.interv % (freq) no 72.7 (117) 83.0 ( 967) 0.004 0.2
yes 27.3 ( 44) 16.7 ( 195)
<NA> 0.0 ( 0) 0.3 ( 3)
EP_composite % (freq) No data available. 0.0 ( 0) 0.0 ( 0) NaN 0.8
No composite endpoints 68.3 (110) 73.8 ( 860)
Composite endpoints 31.7 ( 51) 25.2 ( 294)
<NA> 0.0 ( 0) 0.9 ( 11)
EP_composite_time (mean (SD)) 2.579 (0.961) 2.611 (1.130) 0.735 1.0
macmean0 (mean (SD)) 0.802 (1.072) 0.822 (1.275) 0.856 2.2
smcmean0 (mean (SD)) 2.445 (2.594) 1.923 (2.234) 0.007 2.5
Macrophages.bin % (freq) no/minor 50.3 ( 81) 45.8 ( 533) 0.309 1.8
moderate/heavy 49.1 ( 79) 52.3 ( 609)
<NA> 0.6 ( 1) 2.0 ( 23)
SMC.bin % (freq) no/minor 21.7 ( 35) 32.5 ( 379) 0.017 1.7
moderate/heavy 77.0 (124) 65.8 ( 766)
<NA> 1.2 ( 2) 1.7 ( 20)
neutrophils (mean (SD)) 133.447 (437.032) 158.140 (448.512) 0.754 80.9
Mast_cells_plaque (mean (SD)) 123.389 (135.924) 173.244 (168.601) 0.097 83.7
IPH.bin % (freq) no 39.1 ( 63) 36.3 ( 423) 0.512 1.5
yes 60.2 ( 97) 62.1 ( 723)
<NA> 0.6 ( 1) 1.6 ( 19)
vessel_density_averaged (mean (SD)) 8.837 (6.727) 8.439 (6.394) 0.480 8.0
[ reached getOption("max.print") -- omitted 21 rows ]CEA patients with plasma MCP1 levels
Showing the baseline table of the CEA patients in the Athero-Express Biobank with plasma MCP1 levels.
NOT AVAILABLE YET
AEDB.CEA.subset.serum <- subset(AEDB.CEA, !is.na(MCP1_plasma))
AEDB.CEA.subset.serum.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
strata = "AsymptSympt2G",
data = AEDB.CEA.subset.serum, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:6]CEA patients with plasma and plaque MCP1 levels
Showing the baseline table of the CEA patients in the Athero-Express Biobank with both plasma and plaque MCP1 levels.
NOT AVAILABLE YET
AEDB.CEA.subset.both <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015) & !is.na(MCP1))
AEDB.CEA.subset.both.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
strata = "AsymptSympt2G",
data = AEDB.CEA.subset.both, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:6]Writing the baseline table to Excel format.
# Write basetable
require(openxlsx)Loading required package: openxlsxwrite.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.xlsx"),
AEDB.CEA.tableOne,
row.names = TRUE,
col.names = TRUE,
sheetName = "wholeAEDB_Baseline")
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.AsymptSympt.xlsx"),
AEDB.CEA.subset.AsymptSympt.tableOne,
row.names = TRUE,
col.names = TRUE,
sheetName = "wholeAEDB_Baseline_Sympt")
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEA.xlsx"),
AEDB.CEA.subset.combo.tableOne,
row.names = TRUE,
col.names = TRUE,
sheetName = "subsetAEDB_Baseline")
# write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEAserum.AsymptSympt.xlsx"),
# AEDB.CEA.subset.serum.tableOne,
# row.names = TRUE,
# col.names = TRUE,
# sheetName = "subsetAEDB_Baseline_serum_Sympt")
Data exploration
Here we inspect the data and when necessary transform quantitative measures. We will inspect the raw, natural log transformed + the smallest measurement, and inverse-normal transformation.
MCP1 plaque levels
We will explore the plaque levels. As noted above, we will use MCP1_pg_ug_2015.
summary(AEDB.CEA$MCP1_pg_ug_2015) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0005 0.1373 0.3401 0.6121 0.7232 10.8540 1225 do.call(rbind , by(AEDB.CEA$MCP1_pg_ug_2015, AEDB.CEA$AsymptSympt2G, summary)) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
Asymptomatic 0.0061401090 0.09779678 0.2148984 0.4950529 0.4982360 5.761795 139
Symptomatic 0.0004584575 0.14408036 0.3510514 0.6264834 0.7412862 10.853968 1086library(patchwork)
Attaching package: ‘patchwork’
The following object is masked from ‘package:MASS’:
areap1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
# add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 plaque levels",
xlab = "pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.min_MCP1_pg_ug_2015 <- min(AEDB.CEA$MCP1_pg_ug_2015, na.rm = TRUE)
min_MCP1_pg_ug_2015[1] 0.0004584575AEDB.CEA$MCP1_pg_ug_2015_LN <- log(AEDB.CEA$MCP1_pg_ug_2015 + min_MCP1_pg_ug_2015)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
# add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
# title = "MCP1 plaque levels",
xlab = "natural log-transformed pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.AEDB.CEA$MCP1_pg_ug_2015_rank <- qnorm((rank(AEDB.CEA$MCP1_pg_ug_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1_pg_ug_2015)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 plaque levels",
xlab = "inverse-normal transformation pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.p1 
p2 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "") | ggpar(p3, legend = "right")
rm(p1, p2, p3)MCP1 serum levels
NOT AVAILABLE YET
summary(AEDB.CEA$MCP1)
do.call(rbind , by(AEDB.CEA$MCP1, AEDB.CEA$AsymptSympt2G, summary))
do.call(rbind , by(AEDB.CEA.subset.serum$MCP1_pg_ug_2015, AEDB.CEA.subset.serum$AsymptSympt2G, summary))
do.call(rbind , by(AEDB.CEA.subset.serum$MCP1, AEDB.CEA.subset.serum$AsymptSympt2G, summary))
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
# add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 serum levels",
xlab = "pg/mL",
ggtheme = theme_minimal())
min_MCP1 <- min(AEDB.CEA$MCP1, na.rm = TRUE)
min_MCP1
AEDB.CEA$MCP1_LN <- log(AEDB.CEA$MCP1 + min_MCP1)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
# add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 serum levels",
xlab = "natural log-transformed pg/ug",
ggtheme = theme_minimal())
AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 serum levels",
xlab = "inverse-normal transformation pg/ug",
ggtheme = theme_minimal())
p1
p2
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "") | ggpar(p3, legend = "right")
rm(p1, p2, p3)
Preliminary conclusion data exploration
In line with the previous work by Marios Georgakis we will apply natural log transformation on all proteins and focus the analysis on MCP1 in serum and plaque.
Analyses
The analyses are focused on three elements:
- plaque vulnerability phenotypes
- clinical status at inclusion (symptoms)
- secondary clinical outcome during three (3) years of follow-up
Covariates & other variables
- Age (continuous in 1-year increment). [
Age] - Sex (male vs. female). [
Gender] - Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [
Hypertension.composite] - Presence of diabetes mellitus at baseline (defined either as a history of diabetes and/or administration of glucose lowering medication). [
DiabetesStatus] - Smoking (current, ex-, never). [
SmokerStatus] - LDL-C levels (continuous). [
LDL_final] - Use of lipid-lowering drugs. [
Med.Statin.LLD] - Use of antiplatelet drugs. [
Med.all.antiplatelet] - eGFR (continuous). [
GFR_MDRD] - BMI (continuous). [
BMI] - History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [
MedHx_CVD] combination of [CAD_history,Stroke_history,Peripheral.interv] - Level of stenosis (50-70% vs. 70-99%). [
stenose]
Models
We will analyze the data through four different models
- Model 1: adjusted for age and sex
- Model 2: adjusted for age, sex, and additionally adjusted for history hypertension (defined from medical history and/or use of antihypertensive medications), diabetes (defined as history of a diagnosis and/or use of glucose-lowering medications), current smoking, LDL-C levels at time of operation, use of statins, use of antiplatelet agents, eGFR, BMI, history of cardiovascular disease (coronary artery disease, stroke, peripheral artery disease), and level of stenosis (50-70%, 70-90%, 90-99%)
A. Cross-sectional analysis plaque phenotypes
In the cross-sectional analysis of plaque and serum MCP1, IL6, and IL6R levels we will focus on the following plaque vulnerability phenotypes:
- Percentage of macrophages (continuous trait)
- Percentage of SMCs (continuous trait)
- Number of intraplaque microvessels per 3-4 hotspots (continuous trait)
- Presence of moderate/heavy calcifications (binary trait)
- Presence of moderate/heavy collagen content (binary trait)
- Presence of lipid core no/<10% vs. >10% (binary trait)
- Presence of intraplaque hemorrhage (binary trait)
Continous traits
# macrophages
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$macmean0) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0000 0.0733 0.3133 0.7676 1.0000 15.1000 720 min_macmean <- min(AEDB.CEA$macmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % macrophages: ",min_macmean,".\n"))
Minimum value % macrophages: 0.AEDB.CEA$Macrophages_LN <- log(AEDB.CEA$macmean0 + min_macmean)
ggpubr::gghistogram(AEDB.CEA, "Macrophages_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "% macrophages",
xlab = "natural log-transformed %",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
AEDB.CEA$Macrophages_rank <- qnorm((rank(AEDB.CEA$macmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$macmean0)))
ggpubr::gghistogram(AEDB.CEA, "Macrophages_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "% macrophages",
xlab = "inverse-rank normalized %",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
# smooth muscle cells
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$macmean0) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0000 0.0733 0.3133 0.7676 1.0000 15.1000 720 min_smcmean <- min(AEDB.CEA$smcmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % smooth muscle cells: ",min_smcmean,".\n"))
Minimum value % smooth muscle cells: 0.AEDB.CEA$SMC_LN <- log(AEDB.CEA$smcmean0 + min_smcmean)
ggpubr::gghistogram(AEDB.CEA, "SMC_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "% smooth muscle cells",
xlab = "natural log-transformed %",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
AEDB.CEA$SMC_rank <- qnorm((rank(AEDB.CEA$smcmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$smcmean0)))
ggpubr::gghistogram(AEDB.CEA, "SMC_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "% smooth muscle cells",
xlab = "inverse-rank normalized %",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
# vessel density
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$vessel_density_averaged) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 4.000 7.000 8.318 11.300 48.000 850 min_vesseldensity <- min(AEDB.CEA$vessel_density_averaged, na.rm = TRUE)
min_vesseldensity[1] 0cat(paste0("\nMinimum value number of intraplaque neovessels per 3-4 hotspots: ",min_vesseldensity,".\n"))
Minimum value number of intraplaque neovessels per 3-4 hotspots: 0.AEDB.CEA$VesselDensity_LN <- log(AEDB.CEA$vessel_density_averaged + min_vesseldensity)
ggpubr::gghistogram(AEDB.CEA, "VesselDensity_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "number of intraplaque neovessels per 3-4 hotspots",
xlab = "natural log-transformed number",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
AEDB.CEA$VesselDensity_rank <- qnorm((rank(AEDB.CEA$vessel_density_averaged, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$vessel_density_averaged)))
ggpubr::gghistogram(AEDB.CEA, "VesselDensity_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "number of intraplaque neovessels per 3-4 hotspots",
xlab = "inverse-rank normalized number",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
Binary traits
# calcification
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$Calc.bin) no/minor moderate/heavy NA's
1006 849 566 contrasts(AEDB.CEA$Calc.bin) moderate/heavy
no/minor 0
moderate/heavy 1AEDB.CEA$CalcificationPlaque <- as.factor(AEDB.CEA$Calc.bin)
df <- AEDB.CEA %>%
filter(!is.na(CalcificationPlaque)) %>%
group_by(Gender, CalcificationPlaque) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "CalcificationPlaque", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Calcification",
xlab = "calcification",
ggtheme = theme_minimal())
rm(df)
# collagen
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$Collagen.bin) no/minor moderate/heavy NA's
382 1467 572 contrasts(AEDB.CEA$Collagen.bin) moderate/heavy
no/minor 0
moderate/heavy 1AEDB.CEA$CollagenPlaque <- as.factor(AEDB.CEA$Collagen.bin)
df <- AEDB.CEA %>%
filter(!is.na(CollagenPlaque)) %>%
group_by(Gender, CollagenPlaque) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "CollagenPlaque", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Collagen",
xlab = "collagen",
ggtheme = theme_minimal())
rm(df)
# fat 10%
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$Fat.bin_10) <10% >10% NA's
542 1314 565 contrasts(AEDB.CEA$Fat.bin_10) >10%
<10% 0
>10% 1AEDB.CEA$Fat10Perc <- as.factor(AEDB.CEA$Fat.bin_10)
df <- AEDB.CEA %>%
filter(!is.na(Fat10Perc)) %>%
group_by(Gender, Fat10Perc) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "Fat10Perc", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Intraplaque fat",
xlab = "intraplaque fat",
ggtheme = theme_minimal())
rm(df)
# IPH
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$IPH.bin) no yes NA's
744 1108 569 contrasts(AEDB.CEA$IPH.bin) yes
no 0
yes 1AEDB.CEA$IPH <- as.factor(AEDB.CEA$IPH.bin)
df <- AEDB.CEA %>%
filter(!is.na(IPH)) %>%
group_by(Gender, IPH) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "IPH", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Intraplaque hemorrhage",
xlab = "intraplaque hemorrhage",
ggtheme = theme_minimal())
rm(df)
# Symptoms
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$AsymptSympt) Asymptomatic Ocular and others Symptomatic
270 540 1611 contrasts(AEDB.CEA$AsymptSympt) Ocular and others Symptomatic
Asymptomatic 0 0
Ocular and others 1 0
Symptomatic 0 1AEDB.CEA$AsymptSympt <- as.factor(AEDB.CEA$AsymptSympt)
df <- AEDB.CEA %>%
filter(!is.na(AsymptSympt)) %>%
group_by(Gender, AsymptSympt) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "AsymptSympt", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Symptoms",
xlab = "symptoms",
ggtheme = theme_minimal())
rm(df)
In this section we make some variables to assist with analysis.
AEDB.CEA.samplesize = nrow(AEDB.CEA)
# TRAITS.PROTEIN = c("IL6_LN", "MCP1_LN", "IL6_pg_ug_2015_LN", "IL6R_pg_ug_2015_LN", "MCP1_pg_ug_2015_LN")
# TRAITS.PROTEIN.RANK = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank")
# TRAITS.PROTEIN.RANK = c("MCP1_pg_ug_2015_rank", "MCP1_rank")
TRAITS.PROTEIN.RANK = c("MCP1_pg_ug_2015_rank")
# TRAITS.CON = c("Macrophages_LN", "SMC_LN", "VesselDensity_LN")
TRAITS.CON.RANK = c("Macrophages_rank", "SMC_rank", "VesselDensity_rank")
TRAITS.BIN = c("CalcificationPlaque", "CollagenPlaque", "Fat10Perc", "IPH")
# "Hospital",
# "Age", "Gender",
# "TC_final", "LDL_final", "HDL_final", "TG_final",
# "systolic", "diastoli", "GFR_MDRD", "BMI",
# "KDOQI", "BMI_WHO",
# "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
# "DiabetesStatus", "Hypertension.composite",
# "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
# "Stroke_Dx", "sympt", "Symptoms.5G", "restenos",
# "EP_composite", "EP_composite_time",
# "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
# "neutrophils", "Mast_cells_plaque",
# "IPH.bin", "vessel_density_averaged",
# "Calc.bin", "Collagen.bin",
# "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
# "IL6_pg_ug_2015", "MCP1_pg_ug_2015",
# "QC2018_FILTER", "CHIP", "SAMPLE_TYPE",
# "CAD_history", "Stroke_history", "Peripheral.interv",
# "stenose"
# 1. Age (continuous in 1-year increment). [Age]
# 2. Sex (male vs. female). [Gender]
# 3. Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
# 4. Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
# 5. Smoking (current, ex-, never). [SmokerCurrent]
# 6. LDL-C levels (continuous). [LDL_final]
# 7. Use of lipid-lowering drugs. [Med.Statin.LLD]
# 8. Use of antiplatelet drugs. [Med.all.antiplatelet]
# 9. eGFR (continuous). [GFR_MDRD]
# 10. BMI (continuous). [BMI]
# 11. History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [MedHx_CVD] combinatino of: [CAD_history, Stroke_history, Peripheral.interv]
# 12. Level of stenosis (50-70% vs. 70-99%). [stenose]
# Models
# Model 1: adjusted for age and sex
# Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,
COVARIATES_M1 = c("Age", "Gender")
COVARIATES_M2 = c(COVARIATES_M1,
"Hypertension.composite", "DiabetesStatus",
"SmokerStatus",
# "SmokerCurrent",
"Med.Statin.LLD", "Med.all.antiplatelet",
"GFR_MDRD", "BMI",
# "CAD_history", "Stroke_history", "Peripheral.interv",
"MedHx_CVD",
"stenose")
# COVARIATES_M3 = c(COVARIATES_M2, "LDL_final")
# COVARIATES_M4 = c(COVARIATES_M2, "hsCRP_plasma")
# COVARIATES_M5 = c(COVARIATES_M2, "IL6_pg_ug_2015_LN")
# COVARIATES_M5rank = c(COVARIATES_M2, "IL6_pg_ug_2015_rank")
Model 1
In this model we correct for Age and Gender.
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Quantitative plaque traits
Analysis of continuous/quantitative plaque traits as a function of serum/plaque MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.CON.RANK)) {
TRAIT = TRAITS.CON.RANK[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender, data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of MCP1_pg_ug_2015_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale
-0.08694 -0.04450 0.10580
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4082 -0.6842 0.0046 0.6589 3.3363
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0417401 0.2249607 -0.186 0.8528
currentDF[, TRAIT] -0.0450829 0.0294144 -1.533 0.1256
Age -0.0006612 0.0031991 -0.207 0.8363
Gendermale 0.1060371 0.0635635 1.668 0.0955 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9995 on 1164 degrees of freedom
Multiple R-squared: 0.004088, Adjusted R-squared: 0.001522
F-statistic: 1.593 on 3 and 1164 DF, p-value: 0.1894
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: -0.045083
Standard error............: 0.029414
Odds ratio (effect size)..: 0.956
Lower 95% CI..............: 0.902
Upper 95% CI..............: 1.013
T-value...................: -1.532681
P-value...................: 0.1256263
R^2.......................: 0.004088
Adjusted r^2..............: 0.001522
Sample size of AE DB......: 2421
Sample size of model......: 1168
Missing data %............: 51.75547
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
-0.01596 -0.11159
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4354 -0.6542 -0.0098 0.6350 3.3670
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.112040 0.228792 0.490 0.624438
currentDF[, TRAIT] -0.111735 0.030680 -3.642 0.000283 ***
Age -0.002497 0.003236 -0.772 0.440423
Gendermale 0.062484 0.063873 0.978 0.328152
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9952 on 1160 degrees of freedom
Multiple R-squared: 0.01319, Adjusted R-squared: 0.01064
F-statistic: 5.169 on 3 and 1160 DF, p-value: 0.001498
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.111735
Standard error............: 0.03068
Odds ratio (effect size)..: 0.894
Lower 95% CI..............: 0.842
Upper 95% CI..............: 0.95
T-value...................: -3.641935
P-value...................: 0.000282537
R^2.......................: 0.013191
Adjusted r^2..............: 0.010639
Sample size of AE DB......: 2421
Sample size of model......: 1164
Missing data %............: 51.92069
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale
-0.1146 -0.1291 0.1266
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2793 -0.6743 0.0015 0.6370 3.4081
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.142513 0.233373 -0.611 0.5415
currentDF[, TRAIT] -0.129023 0.030547 -4.224 2.6e-05 ***
Age 0.000407 0.003312 0.123 0.9022
Gendermale 0.126560 0.065772 1.924 0.0546 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.002 on 1088 degrees of freedom
Multiple R-squared: 0.01916, Adjusted R-squared: 0.01645
F-statistic: 7.084 on 3 and 1088 DF, p-value: 0.0001025
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.129023
Standard error............: 0.030547
Odds ratio (effect size)..: 0.879
Lower 95% CI..............: 0.828
Upper 95% CI..............: 0.933
T-value...................: -4.223732
P-value...................: 2.60385e-05
R^2.......................: 0.019159
Adjusted r^2..............: 0.016454
Sample size of AE DB......: 2421
Sample size of model......: 1092
Missing data %............: 54.89467 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Binary plaque traits
Analysis of binary plaque traits as a function of serum/plaque MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.BIN)) {
TRAIT = TRAITS.BIN[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of MCP1_pg_ug_2015_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale
-0.88365 -0.44949 0.01414 -0.19877
Degrees of Freedom: 1177 Total (i.e. Null); 1174 Residual
Null Deviance: 1632
Residual Deviance: 1570 AIC: 1578
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7790 -1.1167 -0.7616 1.1294 1.9050
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.883647 0.460710 -1.918 0.0551 .
currentDF[, PROTEIN] -0.449493 0.062887 -7.148 8.83e-13 ***
Age 0.014139 0.006548 2.159 0.0308 *
Gendermale -0.198771 0.129909 -1.530 0.1260
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1632.5 on 1177 degrees of freedom
Residual deviance: 1570.1 on 1174 degrees of freedom
AIC: 1578.1
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.449493
Standard error............: 0.062887
Odds ratio (effect size)..: 0.638
Lower 95% CI..............: 0.564
Upper 95% CI..............: 0.722
Z-value...................: -7.147681
P-value...................: 8.825609e-13
Hosmer and Lemeshow r^2...: 0.038193
Cox and Snell r^2.........: 0.051552
Nagelkerke's pseudo r^2...: 0.068747
Sample size of AE DB......: 2421
Sample size of model......: 1178
Missing data %............: 51.34242
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN],
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN]
1.3310 -0.2324
Degrees of Freedom: 1178 Total (i.e. Null); 1177 Residual
Null Deviance: 1216
Residual Deviance: 1205 AIC: 1209
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1020 0.5729 0.6568 0.7158 0.9553
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.143689 0.546619 2.092 0.03641 *
currentDF[, PROTEIN] -0.231697 0.072337 -3.203 0.00136 **
Age 0.002892 0.007791 0.371 0.71047
Gendermale -0.015641 0.156179 -0.100 0.92023
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1215.6 on 1178 degrees of freedom
Residual deviance: 1205.0 on 1175 degrees of freedom
AIC: 1213
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.231697
Standard error............: 0.072337
Odds ratio (effect size)..: 0.793
Lower 95% CI..............: 0.688
Upper 95% CI..............: 0.914
Z-value...................: -3.202999
P-value...................: 0.001360044
Hosmer and Lemeshow r^2...: 0.008726
Cox and Snell r^2.........: 0.008956
Nagelkerke's pseudo r^2...: 0.013921
Sample size of AE DB......: 2421
Sample size of model......: 1179
Missing data %............: 51.30112
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale
-0.32933 0.15569 0.01093 0.85630
Degrees of Freedom: 1178 Total (i.e. Null); 1175 Residual
Null Deviance: 1386
Residual Deviance: 1339 AIC: 1347
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9399 -1.2794 0.6824 0.7695 1.2262
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.329327 0.501681 -0.656 0.5115
currentDF[, PROTEIN] 0.155685 0.066807 2.330 0.0198 *
Age 0.010928 0.007185 1.521 0.1283
Gendermale 0.856297 0.137117 6.245 4.24e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1386.5 on 1178 degrees of freedom
Residual deviance: 1338.8 on 1175 degrees of freedom
AIC: 1346.8
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.155685
Standard error............: 0.066807
Odds ratio (effect size)..: 1.168
Lower 95% CI..............: 1.025
Upper 95% CI..............: 1.332
Z-value...................: 2.330372
P-value...................: 0.01978647
Hosmer and Lemeshow r^2...: 0.034376
Cox and Snell r^2.........: 0.039619
Nagelkerke's pseudo r^2...: 0.057296
Sample size of AE DB......: 2421
Sample size of model......: 1179
Missing data %............: 51.30112
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale
0.02945 -0.12895 0.61756
Degrees of Freedom: 1175 Total (i.e. Null); 1173 Residual
Null Deviance: 1572
Residual Deviance: 1546 AIC: 1552
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.650 -1.271 0.884 0.956 1.342
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.157708 0.462022 -0.341 0.7328
currentDF[, PROTEIN] -0.128693 0.060765 -2.118 0.0342 *
Age 0.002737 0.006577 0.416 0.6773
Gendermale 0.616805 0.129037 4.780 1.75e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1572.3 on 1175 degrees of freedom
Residual deviance: 1545.6 on 1172 degrees of freedom
AIC: 1553.6
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IPH
Effect size...............: -0.128693
Standard error............: 0.060765
Odds ratio (effect size)..: 0.879
Lower 95% CI..............: 0.781
Upper 95% CI..............: 0.99
Z-value...................: -2.117883
P-value...................: 0.03418496
Hosmer and Lemeshow r^2...: 0.016998
Cox and Snell r^2.........: 0.022471
Nagelkerke's pseudo r^2...: 0.030474
Sample size of AE DB......: 2421
Sample size of model......: 1176
Missing data %............: 51.42503 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Model 2
In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis.
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Quantitative plaque traits
Analysis of continuous/quantitative plaque traits as a function of serum/plaque MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.CON.RANK)) {
TRAIT = TRAITS.CON.RANK[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender +
Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose,
data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of MCP1_pg_ug_2015_rank.
- processing Macrophages_rankNote: Using an external vector in selections is ambiguous.
[34mℹ[39m Use `all_of(COVARIATES_M2)` instead of `COVARIATES_M2` to silence this message.
[34mℹ[39m See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
[90mThis message is displayed once per session.[39m
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Hypertension.compositeyes Med.Statin.LLDyes
0.30069 -0.05373 -0.20206 -0.14604
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3053 -0.6548 -0.0242 0.6293 3.3436
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.2390578 0.6042876 0.396 0.6925
currentDF[, TRAIT] -0.0612856 0.0317785 -1.929 0.0541 .
Age -0.0022303 0.0039334 -0.567 0.5708
Gendermale 0.0974448 0.0709000 1.374 0.1696
Hypertension.compositeyes -0.2024463 0.0966092 -2.096 0.0364 *
DiabetesStatusDiabetes -0.0289768 0.0772772 -0.375 0.7078
SmokerStatusEx-smoker -0.0230909 0.0731098 -0.316 0.7522
SmokerStatusNever smoked 0.1337644 0.1030877 1.298 0.1947
Med.Statin.LLDyes -0.1502085 0.0774112 -1.940 0.0526 .
Med.all.antiplateletyes 0.0018191 0.1086828 0.017 0.9866
GFR_MDRD -0.0005437 0.0016784 -0.324 0.7461
BMI -0.0026857 0.0088250 -0.304 0.7609
MedHx_CVDyes 0.0199269 0.0664034 0.300 0.7642
stenose50-70% 0.2704089 0.4317030 0.626 0.5312
stenose70-90% 0.3081981 0.4140028 0.744 0.4568
stenose90-99% 0.1802418 0.4140082 0.435 0.6634
stenose100% (Occlusion) -0.2457719 0.5317403 -0.462 0.6440
stenose50-99% 0.4778476 0.6484366 0.737 0.4613
stenose70-99% 0.4432860 0.5811508 0.763 0.4458
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.002 on 1000 degrees of freedom
Multiple R-squared: 0.0227, Adjusted R-squared: 0.005108
F-statistic: 1.29 on 18 and 1000 DF, p-value: 0.1851
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: -0.061286
Standard error............: 0.031778
Odds ratio (effect size)..: 0.941
Lower 95% CI..............: 0.884
Upper 95% CI..............: 1.001
T-value...................: -1.928524
P-value...................: 0.05407278
R^2.......................: 0.0227
Adjusted r^2..............: 0.005108
Sample size of AE DB......: 2421
Sample size of model......: 1019
Missing data %............: 57.90995
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Hypertension.compositeyes Med.Statin.LLDyes
0.3084 -0.1064 -0.2034 -0.1530
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3130 -0.6745 -0.0099 0.6299 3.3696
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.3219731 0.6046171 0.533 0.59448
currentDF[, TRAIT] -0.1029968 0.0329730 -3.124 0.00184 **
Age -0.0034454 0.0039631 -0.869 0.38485
Gendermale 0.0487194 0.0717747 0.679 0.49743
Hypertension.compositeyes -0.1954166 0.0965630 -2.024 0.04327 *
DiabetesStatusDiabetes -0.0219028 0.0772121 -0.284 0.77672
SmokerStatusEx-smoker -0.0209122 0.0731694 -0.286 0.77509
SmokerStatusNever smoked 0.1054769 0.1030387 1.024 0.30624
Med.Statin.LLDyes -0.1609021 0.0774630 -2.077 0.03804 *
Med.all.antiplateletyes -0.0039324 0.1085928 -0.036 0.97112
GFR_MDRD -0.0004018 0.0016792 -0.239 0.81092
BMI -0.0026011 0.0088289 -0.295 0.76836
MedHx_CVDyes 0.0116355 0.0664377 0.175 0.86101
stenose50-70% 0.3063875 0.4313296 0.710 0.47766
stenose70-90% 0.3397682 0.4136695 0.821 0.41164
stenose90-99% 0.2223931 0.4138010 0.537 0.59108
stenose100% (Occlusion) -0.1771374 0.5313862 -0.333 0.73894
stenose50-99% 0.5447894 0.6479149 0.841 0.40064
stenose70-99% 0.4871557 0.5806444 0.839 0.40168
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.001 on 996 degrees of freedom
Multiple R-squared: 0.02833, Adjusted R-squared: 0.01077
F-statistic: 1.613 on 18 and 996 DF, p-value: 0.05023
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.102997
Standard error............: 0.032973
Odds ratio (effect size)..: 0.902
Lower 95% CI..............: 0.846
Upper 95% CI..............: 0.962
T-value...................: -3.123675
P-value...................: 0.00183768
R^2.......................: 0.028328
Adjusted r^2..............: 0.010768
Sample size of AE DB......: 2421
Sample size of model......: 1015
Missing data %............: 58.07518
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Hypertension.compositeyes Med.Statin.LLDyes
0.2912 -0.1439 -0.1849 -0.1662
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1614 -0.6484 -0.0233 0.6402 3.4332
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.1767459 0.6402229 0.276 0.7826
currentDF[, TRAIT] -0.1420732 0.0332558 -4.272 2.13e-05 ***
Age -0.0009175 0.0040589 -0.226 0.8212
Gendermale 0.1109606 0.0733445 1.513 0.1307
Hypertension.compositeyes -0.1942298 0.1002766 -1.937 0.0531 .
DiabetesStatusDiabetes -0.0328877 0.0819413 -0.401 0.6882
SmokerStatusEx-smoker -0.0313111 0.0760495 -0.412 0.6806
SmokerStatusNever smoked 0.1139310 0.1071394 1.063 0.2879
Med.Statin.LLDyes -0.1671740 0.0796915 -2.098 0.0362 *
Med.all.antiplateletyes 0.0397565 0.1144791 0.347 0.7285
GFR_MDRD -0.0010896 0.0017515 -0.622 0.5340
BMI -0.0016953 0.0091572 -0.185 0.8532
MedHx_CVDyes 0.0074801 0.0688297 0.109 0.9135
stenose50-70% 0.1841594 0.4727540 0.390 0.6970
stenose70-90% 0.2563949 0.4542951 0.564 0.5726
stenose90-99% 0.1436685 0.4538378 0.317 0.7516
stenose100% (Occlusion) -0.2562619 0.5637405 -0.455 0.6495
stenose50-99% 0.5911304 0.6754523 0.875 0.3817
stenose70-99% 0.1673943 0.6766921 0.247 0.8047
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.005 on 932 degrees of freedom
Multiple R-squared: 0.03977, Adjusted R-squared: 0.02122
F-statistic: 2.144 on 18 and 932 DF, p-value: 0.003707
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.142073
Standard error............: 0.033256
Odds ratio (effect size)..: 0.868
Lower 95% CI..............: 0.813
Upper 95% CI..............: 0.926
T-value...................: -4.272134
P-value...................: 2.134847e-05
R^2.......................: 0.039768
Adjusted r^2..............: 0.021223
Sample size of AE DB......: 2421
Sample size of model......: 951
Missing data %............: 60.71871 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Binary plaque traits
Analysis of binary plaque traits as a function of serum/plaque MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.BIN)) {
TRAIT = TRAITS.BIN[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender +
Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of MCP1_pg_ug_2015_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + SmokerStatus + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age SmokerStatusEx-smoker SmokerStatusNever smoked stenose50-70% stenose70-90%
-1.16173 -0.47346 0.01896 -0.40609 -0.44306 -0.56758 -0.01349
stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99%
0.18538 1.15456 -14.43672 -0.79787
Degrees of Freedom: 1023 Total (i.e. Null); 1013 Residual
Null Deviance: 1417
Residual Deviance: 1335 AIC: 1357
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8447 -1.0810 -0.7025 1.1078 2.0019
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.522e+00 1.248e+00 -1.220 0.22262
currentDF[, PROTEIN] -4.740e-01 6.875e-02 -6.895 5.38e-12 ***
Age 1.797e-02 8.224e-03 2.185 0.02892 *
Gendermale -9.953e-02 1.469e-01 -0.677 0.49809
Hypertension.compositeyes 2.389e-01 2.017e-01 1.185 0.23618
DiabetesStatusDiabetes -2.266e-01 1.619e-01 -1.400 0.16160
SmokerStatusEx-smoker -4.096e-01 1.525e-01 -2.687 0.00722 **
SmokerStatusNever smoked -4.851e-01 2.157e-01 -2.249 0.02454 *
Med.Statin.LLDyes -1.636e-01 1.607e-01 -1.019 0.30842
Med.all.antiplateletyes -7.738e-02 2.248e-01 -0.344 0.73071
GFR_MDRD -5.302e-04 3.511e-03 -0.151 0.87997
BMI 2.166e-02 1.839e-02 1.178 0.23892
MedHx_CVDyes -1.660e-02 1.377e-01 -0.121 0.90406
stenose50-70% -5.216e-01 8.838e-01 -0.590 0.55510
stenose70-90% 9.803e-03 8.419e-01 0.012 0.99071
stenose90-99% 2.028e-01 8.420e-01 0.241 0.80969
stenose100% (Occlusion) 1.181e+00 1.175e+00 1.005 0.31496
stenose50-99% -1.444e+01 4.192e+02 -0.034 0.97252
stenose70-99% -7.533e-01 1.208e+00 -0.624 0.53294
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1417.5 on 1023 degrees of freedom
Residual deviance: 1329.0 on 1005 degrees of freedom
AIC: 1367
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.474031
Standard error............: 0.068749
Odds ratio (effect size)..: 0.622
Lower 95% CI..............: 0.544
Upper 95% CI..............: 0.712
Z-value...................: -6.895134
P-value...................: 5.381419e-12
Hosmer and Lemeshow r^2...: 0.062418
Cox and Snell r^2.........: 0.082776
Nagelkerke's pseudo r^2...: 0.110443
Sample size of AE DB......: 2421
Sample size of model......: 1024
Missing data %............: 57.70343
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
SmokerStatus + BMI + MedHx_CVD, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] SmokerStatusEx-smoker SmokerStatusNever smoked BMI MedHx_CVDyes
0.47638 -0.24086 -0.38304 -0.63224 0.03905 0.25051
Degrees of Freedom: 1024 Total (i.e. Null); 1019 Residual
Null Deviance: 1048
Residual Deviance: 1025 AIC: 1037
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2698 0.4534 0.6199 0.7192 1.1840
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.385e+01 9.628e+02 0.014 0.98852
currentDF[, PROTEIN] -2.330e-01 7.922e-02 -2.941 0.00327 **
Age 1.362e-02 9.731e-03 1.400 0.16155
Gendermale -4.542e-02 1.763e-01 -0.258 0.79672
Hypertension.compositeyes 2.446e-01 2.286e-01 1.070 0.28467
DiabetesStatusDiabetes 7.902e-02 1.972e-01 0.401 0.68861
SmokerStatusEx-smoker -4.387e-01 1.893e-01 -2.317 0.02051 *
SmokerStatusNever smoked -7.570e-01 2.485e-01 -3.046 0.00232 **
Med.Statin.LLDyes 2.795e-03 1.921e-01 0.015 0.98839
Med.all.antiplateletyes 2.526e-01 2.597e-01 0.973 0.33067
GFR_MDRD 5.011e-03 4.226e-03 1.186 0.23576
BMI 4.166e-02 2.314e-02 1.801 0.07175 .
MedHx_CVDyes 2.219e-01 1.635e-01 1.357 0.17468
stenose50-70% -1.480e+01 9.628e+02 -0.015 0.98774
stenose70-90% -1.509e+01 9.628e+02 -0.016 0.98750
stenose90-99% -1.519e+01 9.628e+02 -0.016 0.98741
stenose100% (Occlusion) 1.169e-01 1.242e+03 0.000 0.99992
stenose50-99% -3.410e-02 1.515e+03 0.000 0.99998
stenose70-99% -1.464e+01 9.628e+02 -0.015 0.98787
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1047.6 on 1024 degrees of freedom
Residual deviance: 1010.2 on 1006 degrees of freedom
AIC: 1048.2
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.232976
Standard error............: 0.079216
Odds ratio (effect size)..: 0.792
Lower 95% CI..............: 0.678
Upper 95% CI..............: 0.925
Z-value...................: -2.941031
P-value...................: 0.003271218
Hosmer and Lemeshow r^2...: 0.035759
Cox and Snell r^2.........: 0.035889
Nagelkerke's pseudo r^2...: 0.056063
Sample size of AE DB......: 2421
Sample size of model......: 1025
Missing data %............: 57.66212
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + SmokerStatus + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale SmokerStatusEx-smoker SmokerStatusNever smoked stenose50-70% stenose70-90%
13.8072 0.1608 0.9217 -0.2543 0.3188 -13.4987 -13.3997
stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99%
-13.2648 -13.8075 -15.8024 -14.6344
Degrees of Freedom: 1024 Total (i.e. Null); 1014 Residual
Null Deviance: 1208
Residual Deviance: 1153 AIC: 1175
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0874 -1.2279 0.6773 0.7958 1.5145
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.323e+01 3.540e+02 0.037 0.9702
currentDF[, PROTEIN] 1.538e-01 7.309e-02 2.105 0.0353 *
Age 7.892e-03 8.954e-03 0.881 0.3781
Gendermale 9.325e-01 1.563e-01 5.966 2.43e-09 ***
Hypertension.compositeyes -3.351e-02 2.222e-01 -0.151 0.8802
DiabetesStatusDiabetes -1.599e-01 1.759e-01 -0.909 0.3634
SmokerStatusEx-smoker -2.887e-01 1.683e-01 -1.716 0.0862 .
SmokerStatusNever smoked 2.644e-01 2.474e-01 1.069 0.2852
Med.Statin.LLDyes -2.292e-01 1.845e-01 -1.242 0.2141
Med.all.antiplateletyes 1.095e-01 2.495e-01 0.439 0.6608
GFR_MDRD 4.634e-04 3.879e-03 0.119 0.9049
BMI 4.721e-03 1.983e-02 0.238 0.8118
MedHx_CVDyes 7.604e-02 1.518e-01 0.501 0.6164
stenose50-70% -1.352e+01 3.539e+02 -0.038 0.9695
stenose70-90% -1.339e+01 3.539e+02 -0.038 0.9698
stenose90-99% -1.327e+01 3.539e+02 -0.037 0.9701
stenose100% (Occlusion) -1.382e+01 3.540e+02 -0.039 0.9689
stenose50-99% -1.581e+01 3.540e+02 -0.045 0.9644
stenose70-99% -1.472e+01 3.540e+02 -0.042 0.9668
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1207.9 on 1024 degrees of freedom
Residual deviance: 1149.4 on 1006 degrees of freedom
AIC: 1187.4
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.153844
Standard error............: 0.073089
Odds ratio (effect size)..: 1.166
Lower 95% CI..............: 1.011
Upper 95% CI..............: 1.346
Z-value...................: 2.104886
P-value...................: 0.03530121
Hosmer and Lemeshow r^2...: 0.048471
Cox and Snell r^2.........: 0.055521
Nagelkerke's pseudo r^2...: 0.080203
Sample size of AE DB......: 2421
Sample size of model......: 1025
Missing data %............: 57.66212
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Med.Statin.LLD + BMI + MedHx_CVD, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Med.Statin.LLDyes BMI MedHx_CVDyes
-0.71077 -0.14275 0.51657 -0.27539 0.03056 0.36067
Degrees of Freedom: 1022 Total (i.e. Null); 1017 Residual
Null Deviance: 1367
Residual Deviance: 1337 AIC: 1349
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9526 -1.2816 0.8168 0.9931 1.5440
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.3744407 1.2806515 -0.292 0.7700
currentDF[, PROTEIN] -0.1420106 0.0661286 -2.147 0.0318 *
Age 0.0002745 0.0081696 0.034 0.9732
Gendermale 0.5755452 0.1456998 3.950 7.81e-05 ***
Hypertension.compositeyes -0.1304980 0.2014410 -0.648 0.5171
DiabetesStatusDiabetes -0.1239874 0.1610422 -0.770 0.4414
SmokerStatusEx-smoker -0.1106836 0.1537985 -0.720 0.4717
SmokerStatusNever smoked -0.1474556 0.2125316 -0.694 0.4878
Med.Statin.LLDyes -0.2598492 0.1641604 -1.583 0.1134
Med.all.antiplateletyes 0.0938507 0.2263983 0.415 0.6785
GFR_MDRD -0.0048629 0.0035299 -1.378 0.1683
BMI 0.0346557 0.0186016 1.863 0.0625 .
MedHx_CVDyes 0.3433350 0.1371721 2.503 0.0123 *
stenose50-70% -0.3005803 0.9270679 -0.324 0.7458
stenose70-90% -0.1234783 0.8929528 -0.138 0.8900
stenose90-99% 0.1233878 0.8933095 0.138 0.8901
stenose100% (Occlusion) -0.3672092 1.1204001 -0.328 0.7431
stenose50-99% -0.5109985 1.3494741 -0.379 0.7049
stenose70-99% 1.3028582 1.4085773 0.925 0.3550
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1367.4 on 1022 degrees of freedom
Residual deviance: 1325.7 on 1004 degrees of freedom
AIC: 1363.7
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IPH
Effect size...............: -0.142011
Standard error............: 0.066129
Odds ratio (effect size)..: 0.868
Lower 95% CI..............: 0.762
Upper 95% CI..............: 0.988
Z-value...................: -2.14749
P-value...................: 0.03175426
Hosmer and Lemeshow r^2...: 0.030475
Cox and Snell r^2.........: 0.039915
Nagelkerke's pseudo r^2...: 0.054139
Sample size of AE DB......: 2421
Sample size of model......: 1023
Missing data %............: 57.74473 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
B. Cross-sectional analysis symptoms
We will perform a cross-sectional analysis between plaque and serum MCP1, IL6, and IL6R levels and the ‘clinical status’ of the plaque in terms of presence of patients’ symptoms (symptomatic vs. asymptomatic). The symptoms of interest are:
- stroke
- TIA
- retinal infarction
- amaurosis fugax
- asymptomatic
Model 1
In this model we correct for Age, and Gender.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "AsymptSympt"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
# + Hypertension.composite + DiabetesStatus + SmokerCurrent +
# Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
# CAD_history + Stroke_history + Peripheral.interv + stenose
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
Analysis of MCP1_pg_ug_2015_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale
0.24776 0.26397 0.03239 -0.43507
Degrees of Freedom: 1195 Total (i.e. Null); 1192 Residual
Null Deviance: 826.5
Residual Deviance: 804.7 AIC: 812.7
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5654 0.3738 0.4440 0.5167 0.8434
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.24776 0.69360 0.357 0.72093
currentDF[, PROTEIN] 0.26397 0.09376 2.815 0.00487 **
Age 0.03239 0.01011 3.203 0.00136 **
Gendermale -0.43507 0.21792 -1.996 0.04588 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 826.52 on 1195 degrees of freedom
Residual deviance: 804.69 on 1192 degrees of freedom
AIC: 812.69
Number of Fisher Scoring iterations: 5
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.263965
Standard error............: 0.09376
Odds ratio (effect size)..: 1.302
Lower 95% CI..............: 1.083
Upper 95% CI..............: 1.565
Z-value...................: 2.815333
P-value...................: 0.004872671
Hosmer and Lemeshow r^2...: 0.026418
Cox and Snell r^2.........: 0.018091
Nagelkerke's pseudo r^2...: 0.036257
Sample size of AE DB......: 2421
Sample size of model......: 1196
Missing data %............: 50.59893 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.Symptoms")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Model 2
In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis..
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "AsymptSympt"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + + stenose,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
Analysis of MCP1_pg_ug_2015_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Med.all.antiplatelet + GFR_MDRD + stenose,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale Med.all.antiplateletyes GFR_MDRD stenose50-70%
15.334896 0.272930 0.032194 -0.427140 -0.929815 0.007921 -13.442880
stenose70-90% stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99%
-15.004949 -14.749153 -0.339553 -15.814682 -0.541412
Degrees of Freedom: 1035 Total (i.e. Null); 1024 Residual
Null Deviance: 726.5
Residual Deviance: 689.3 AIC: 713.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + +stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.0446 0.3116 0.4390 0.5442 0.9328
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.593e+01 9.540e+02 0.017 0.9867
currentDF[, PROTEIN] 2.558e-01 1.018e-01 2.512 0.0120 *
Age 3.508e-02 1.258e-02 2.788 0.0053 **
Gendermale -3.507e-01 2.366e-01 -1.482 0.1383
Hypertension.compositeyes -3.437e-01 3.443e-01 -0.998 0.3182
DiabetesStatusDiabetes -3.937e-02 2.423e-01 -0.162 0.8709
SmokerStatusEx-smoker -3.416e-01 2.335e-01 -1.463 0.1434
SmokerStatusNever smoked 2.040e-04 3.564e-01 0.001 0.9995
Med.Statin.LLDyes -1.797e-01 2.660e-01 -0.676 0.4991
Med.all.antiplateletyes -9.093e-01 4.789e-01 -1.899 0.0576 .
GFR_MDRD 7.160e-03 5.456e-03 1.312 0.1894
BMI -1.027e-02 2.789e-02 -0.368 0.7128
MedHx_CVDyes 9.204e-02 2.096e-01 0.439 0.6606
stenose50-70% -1.338e+01 9.540e+02 -0.014 0.9888
stenose70-90% -1.495e+01 9.540e+02 -0.016 0.9875
stenose90-99% -1.472e+01 9.540e+02 -0.015 0.9877
stenose100% (Occlusion) -3.915e-01 1.230e+03 0.000 0.9997
stenose50-99% -1.592e+01 9.540e+02 -0.017 0.9867
stenose70-99% -5.044e-01 1.190e+03 0.000 0.9997
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 726.47 on 1035 degrees of freedom
Residual deviance: 684.24 on 1017 degrees of freedom
AIC: 722.24
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.255797
Standard error............: 0.10185
Odds ratio (effect size)..: 1.291
Lower 95% CI..............: 1.058
Upper 95% CI..............: 1.577
Z-value...................: 2.511506
P-value...................: 0.01202173
Hosmer and Lemeshow r^2...: 0.058134
Cox and Snell r^2.........: 0.039945
Nagelkerke's pseudo r^2...: 0.079253
Sample size of AE DB......: 2421
Sample size of model......: 1036
Missing data %............: 57.20777 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
C. Longitudinal analysis secondary clinical outcome
For the longitudinal analyses of plaque and serum MCP1, IL6, and IL6R levels and secondary cardiovascular events over a three-year follow-up period.
The primary outcome is defined as “a composite of fatal or non-fatal myocardial infarction, fatal or non-fatal stroke, ruptured aortic aneurysm, fatal cardiac failure, coronary or peripheral interventions, leg amputation due to vascular causes, and cardiovascular death”, i.e. major adverse cardiovascular events (MACE). Variable: epmajor.3years, these include: - myocardial infarction (MI) - cerebral infarction (CVA/stroke) - cardiovascular death (exact cause to be investigated) - cerebral bleeding (CVA/stroke) - fatal myocardial infarction (MI) - fatal cerebral infarction - fatal cerebral bleeding - sudden death - fatal heart failure - fatal aneurysm rupture - other cardiovascular death..
The secondary outcomes will be
- incidence of fatal or non-fatal stroke (ischemic and bleeding) - variable:
epstroke.3years, these include:- cerebral infarction (CVA/stroke)
- cerebral bleeding (CVA/stroke)
- fatal cerebral infarction
- fatal cerebral bleeding.
- incidence of acute coronary events (fatal or non-fatal myocardial infarction, coronary interventions) - variable:
epcoronary.3years, these include:- myocardial infarction (MI)
- coronary angioplasty (PCI/PTCA)
- cardiovascular death (exact cause to be investigated)
- coronary bypass (CABG)
- fatal myocardial infarction (MI)
- sudden death.
- cardiovascular death - variable:
epcvdeath.3years, these include:- cardiovascular death (exact cause to be investigated)
- fatal myocardial infarction (MI)
- fatal cerebral infarction
- fatal cerebral bleeding
- sudden death
- fatal heart failure
- fatal aneurysm rupture
- other cardiovascular death..
30- and 90-days FU events
We will use 3-year follow-up, but we will also calculate 30 days and 90 days follow-up ‘time-to-event’ variables. On average there are 365.25 days in a year. We can calculate 30-days and 90-days follow-up time based on the three years follow-up.
cutt.off.30days = (1/365.25) * 30
cutt.off.90days = (1/365.25) * 90
# Fix maximum FU of 30 and 90 days
AEDB <- AEDB %>%
mutate(
FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
)
AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary",
"max.followup",
"FU.cutt.off.3years",
"FU.cutt.off.30days",
"FU.cutt.off.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
rm(AEDB.temp)
AEDB.CEA <- AEDB.CEA %>%
mutate(
FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
)
AEDB.CEA.temp <- subset(AEDB.CEA, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary",
"max.followup",
"FU.cutt.off.3years",
"FU.cutt.off.30days",
"FU.cutt.off.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)
DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)
rm(AEDB.CEA.temp)
Here we will calculate the new 30- and 90-days follow-up of the events and their event-times of interest:
- MACE (
epmajor.3years) - Stroke (
epstroke.3years) - Coronary events (
epcoronary.3years) - Cardiovascular death (
epcvdeath.3years)
avg_days_in_year = 365.25
cutt.off.30days.scaled <- cutt.off.30days * 365.25
cutt.off.90days.scaled <- cutt.off.90days * 365.25
# Event times
AEDB <- AEDB %>%
mutate(
ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
)
AEDB.CEA <- AEDB.CEA %>%
mutate(
ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
)
attach(AEDB)
AEDB[,"epmajor.30days"] <- AEDB$epmajor.3years
AEDB$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0
AEDB[,"epstroke.30days"] <- AEDB$epstroke.3years
AEDB$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0
AEDB[,"epcoronary.30days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0
AEDB[,"epcvdeath.30days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0
AEDB[,"epmajor.90days"] <- AEDB$epmajor.3years
AEDB$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0
AEDB[,"epstroke.90days"] <- AEDB$epstroke.3years
AEDB$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0
AEDB[,"epcoronary.90days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0
AEDB[,"epcvdeath.90days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0
detach(AEDB)
AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary",
"epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
"epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
"epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
rm(AEDB.temp)
attach(AEDB.CEA)
AEDB.CEA[,"epmajor.30days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0
AEDB.CEA[,"epstroke.30days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0
AEDB.CEA[,"epcoronary.30days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0
AEDB.CEA[,"epcvdeath.30days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0
AEDB.CEA[,"epmajor.90days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0
AEDB.CEA[,"epstroke.90days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0
AEDB.CEA[,"epcoronary.90days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0
AEDB.CEA[,"epcvdeath.90days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0
detach(AEDB.CEA)
AEDB.CEA.temp <- subset(AEDB.CEA, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary",
"epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
"epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
"epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)
DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)
rm(AEDB.CEA.temp)
Sanity checks
First we do some sanity checks and inventory the time-to-event and event variables.
# Reference: https://bioconductor.org/packages/devel/bioc/vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html
# If you want to suppress warnings and messages when installing/loading packages
# suppressPackageStartupMessages({})
install.packages.auto("survival")Loading required package: survivalinstall.packages.auto("survminer")Loading required package: survminerinstall.packages.auto("Hmisc")Loading required package: Hmisc
Loading required package: lattice
Loading required package: Formula
Attaching package: ‘Hmisc’
The following objects are masked from ‘package:dplyr’:
src, summarize
The following objects are masked from ‘package:base’:
format.pval, unitscat("* Creating function to summarize Cox regression and prepare container for results.")* Creating function to summarize Cox regression and prepare container for results.# Function to get summary statistics from Cox regression model
COX.STAT <- function(coxfit, DATASET, OUTCOME, protein){
cat("Summarizing Cox regression results for '", protein ,"' and its association to '",OUTCOME,"' in '",DATASET,"'.\n")
if (nrow(summary(coxfit)$coefficients) == 1) {
output = c(protein, rep(NA,8))
cat("Model not fitted; probably singular.\n")
}else {
cat("Collecting data.\n\n")
cox.sum <- summary(coxfit)
cox.effectsize = cox.sum$coefficients[1,1]
cox.SE = cox.sum$coefficients[1,3]
cox.HReffect = cox.sum$coefficients[1,2]
cox.CI_low = exp(cox.effectsize - 1.96 * cox.SE)
cox.CI_up = exp(cox.effectsize + 1.96 * cox.SE)
cox.zvalue = cox.sum$coefficients[1,4]
cox.pvalue = cox.sum$coefficients[1,5]
cox.sample_size = cox.sum$n
cox.nevents = cox.sum$nevent
output = c(DATASET, OUTCOME, protein, cox.effectsize, cox.SE, cox.HReffect, cox.CI_low, cox.CI_up, cox.zvalue, cox.pvalue, cox.sample_size, cox.nevents)
cat("We have collected the following:\n")
cat("Dataset used..............:", DATASET, "\n")
cat("Outcome analyzed..........:", OUTCOME, "\n")
cat("Protein...................:", protein, "\n")
cat("Effect size...............:", round(cox.effectsize, 6), "\n")
cat("Standard error............:", round(cox.SE, 6), "\n")
cat("Odds ratio (effect size)..:", round(cox.HReffect, 3), "\n")
cat("Lower 95% CI..............:", round(cox.CI_low, 3), "\n")
cat("Upper 95% CI..............:", round(cox.CI_up, 3), "\n")
cat("T-value...................:", round(cox.zvalue, 6), "\n")
cat("P-value...................:", signif(cox.pvalue, 8), "\n")
cat("Sample size in model......:", cox.sample_size, "\n")
cat("Number of events..........:", cox.nevents, "\n")
}
return(output)
print(output)
}
times = c("ep_major_t_3years",
"ep_stroke_t_3years", "ep_coronary_t_3years", "ep_cvdeath_t_3years")
endpoints = c("epmajor.3years",
"epstroke.3years", "epcoronary.3years", "epcvdeath.3years")
cat("* Check the cases per event type - for sanity.")* Check the cases per event type - for sanity.for (events in endpoints){
require(labelled)
print(paste0("Printing the summary of: ",events))
# print(summary(AEDB.CEA[,events]))
print(table(AEDB.CEA[,events]))
}[1] "Printing the summary of: epmajor.3years"
0 1
2033 265
[1] "Printing the summary of: epstroke.3years"
0 1
2169 130
[1] "Printing the summary of: epcoronary.3years"
0 1
2117 182
[1] "Printing the summary of: epcvdeath.3years"
0 1
2208 90 cat("* Check distribution of events over time - for sanity.")* Check distribution of events over time - for sanity.for (eventtimes in times){
print(paste0("Printing the summary of: ",eventtimes))
print(summary(AEDB.CEA[,eventtimes]))
}[1] "Printing the summary of: ep_major_t_3years"
ep_major_t_3years
Min. :0.000
1st Qu.:2.708
Median :3.000
Mean :2.573
3rd Qu.:3.000
Max. :3.000
NA's :125
[1] "Printing the summary of: ep_stroke_t_3years"
ep_stroke_t_3years
Min. :0.000
1st Qu.:2.877
Median :3.000
Mean :2.623
3rd Qu.:3.000
Max. :3.000
NA's :125
[1] "Printing the summary of: ep_coronary_t_3years"
ep_coronary_t_3years
Min. :0.000
1st Qu.:2.783
Median :3.000
Mean :2.622
3rd Qu.:3.000
Max. :3.000
NA's :125
[1] "Printing the summary of: ep_cvdeath_t_3years"
ep_cvdeath_t_3years
Min. :0.00274
1st Qu.:2.91233
Median :3.00000
Mean :2.70878
3rd Qu.:3.00000
Max. :3.00000
NA's :125 for (eventtime in times){
print(paste0("Printing the distribution of: ",eventtime))
p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
main = eventtime, bins = 15,
xlab = "year", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal())
print(p)
ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPerYear.",eventtime,".pdf"), plot = last_plot())
}[1] "Printing the distribution of: ep_major_t_3years"
[1] "Printing the distribution of: ep_stroke_t_3years"
[1] "Printing the distribution of: ep_coronary_t_3years"
[1] "Printing the distribution of: ep_cvdeath_t_3years"
times30 = c("ep_major_t_30days",
"ep_stroke_t_30days", "ep_coronary_t_30days", "ep_cvdeath_t_30days")
endpoints30 = c("epmajor.30days",
"epstroke.30days", "epcoronary.30days", "epcvdeath.30days")
cat("* Check the cases per event type - for sanity.")* Check the cases per event type - for sanity.for (events in endpoints30){
print(paste0("Printing the summary of: ",events))
# print(summary(AEDB.CEA[,events]))
print(table(AEDB.CEA[,events]))
}[1] "Printing the summary of: epmajor.30days"
0 1
2220 78
[1] "Printing the summary of: epstroke.30days"
0 1
2246 53
[1] "Printing the summary of: epcoronary.30days"
0 1
2265 34
[1] "Printing the summary of: epcvdeath.30days"
0 1
2286 12 cat("* Check distribution of events over time - for sanity.")* Check distribution of events over time - for sanity.for (eventtimes in times30){
print(paste0("Printing the summary of: ",eventtimes))
print(summary(AEDB.CEA[,eventtimes]))
}[1] "Printing the summary of: ep_major_t_30days"
ep_major_t_30days
Min. : 0.00
1st Qu.:30.00
Median :30.00
Mean :29.09
3rd Qu.:30.00
Max. :30.00
NA's :125
[1] "Printing the summary of: ep_stroke_t_30days"
ep_stroke_t_30days
Min. : 0.00
1st Qu.:30.00
Median :30.00
Mean :29.32
3rd Qu.:30.00
Max. :30.00
NA's :125
[1] "Printing the summary of: ep_coronary_t_30days"
ep_coronary_t_30days
Min. : 0.00
1st Qu.:30.00
Median :30.00
Mean :29.54
3rd Qu.:30.00
Max. :30.00
NA's :125
[1] "Printing the summary of: ep_cvdeath_t_30days"
ep_cvdeath_t_30days
Min. : 1.001
1st Qu.:30.000
Median :30.000
Mean :29.854
3rd Qu.:30.000
Max. :30.000
NA's :125 for (eventtime in times30){
print(paste0("Printing the distribution of: ",eventtime))
p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
main = eventtime, bins = 15,
xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal())
print(p)
ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPer30Days.",eventtime,".pdf"), plot = last_plot())
}[1] "Printing the distribution of: ep_major_t_30days"
[1] "Printing the distribution of: ep_stroke_t_30days"
[1] "Printing the distribution of: ep_coronary_t_30days"
[1] "Printing the distribution of: ep_cvdeath_t_30days"
times90 = c("ep_major_t_90days",
"ep_stroke_t_90days", "ep_coronary_t_90days", "ep_cvdeath_t_90days")
endpoints90 = c("epmajor.90days",
"epstroke.90days", "epcoronary.90days", "epcvdeath.90days")
cat("* Check the cases per event type - for sanity.")* Check the cases per event type - for sanity.for (events in endpoints90){
print(paste0("Printing the summary of: ",events))
# print(summary(AEDB.CEA[,events]))
print(table(AEDB.CEA[,events]))
}[1] "Printing the summary of: epmajor.90days"
0 1
2204 94
[1] "Printing the summary of: epstroke.90days"
0 1
2239 60
[1] "Printing the summary of: epcoronary.90days"
0 1
2255 44
[1] "Printing the summary of: epcvdeath.90days"
0 1
2279 19 cat("* Check distribution of events over time - for sanity.")* Check distribution of events over time - for sanity.for (eventtimes in times90){
print(paste0("Printing the summary of: ",eventtimes))
print(summary(AEDB.CEA[,eventtimes]))
}[1] "Printing the summary of: ep_major_t_90days"
ep_major_t_90days
Min. : 0.00
1st Qu.:90.00
Median :90.00
Mean :86.74
3rd Qu.:90.00
Max. :90.00
NA's :125
[1] "Printing the summary of: ep_stroke_t_90days"
ep_stroke_t_90days
Min. : 0.00
1st Qu.:90.00
Median :90.00
Mean :87.51
3rd Qu.:90.00
Max. :90.00
NA's :125
[1] "Printing the summary of: ep_coronary_t_90days"
ep_coronary_t_90days
Min. : 0.0
1st Qu.:90.0
Median :90.0
Mean :88.2
3rd Qu.:90.0
Max. :90.0
NA's :125
[1] "Printing the summary of: ep_cvdeath_t_90days"
ep_cvdeath_t_90days
Min. : 1.001
1st Qu.:90.000
Median :90.000
Mean :89.320
3rd Qu.:90.000
Max. :90.000
NA's :125 for (eventtime in times90){
print(paste0("Printing the distribution of: ",eventtime))
p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
main = eventtime, bins = 15,
xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal())
print(p)
ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPer90Days.",eventtime,".pdf"), plot = last_plot())
}[1] "Printing the distribution of: ep_major_t_90days"
[1] "Printing the distribution of: ep_stroke_t_90days"
[1] "Printing the distribution of: ep_coronary_t_90days"
[1] "Printing the distribution of: ep_cvdeath_t_90days"
Cox regressions
Let’s perform the actual Cox-regressions. We will apply a couple of models:
- Model 1: adjusted for age and sex
- Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
3 years follow-up
MODEL 1
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
eptime = times[i]
ep = endpoints[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 1 (Simple model)
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
# ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [years]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL1.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}* Analyzing the effect of plaque proteins on [epmajor.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 1 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34055,0.00105) [ 0.00105,3.34055]
598 598
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 1184, number of events= 139
(1237 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 0.037093 1.037789 0.169988 0.218 0.827268
Age 0.033469 1.034035 0.009876 3.389 0.000702 ***
Gendermale 0.336307 1.399769 0.199676 1.684 0.092131 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 1.038 0.9636 0.7437 1.448
Age 1.034 0.9671 1.0142 1.054
Gendermale 1.400 0.7144 0.9464 2.070
Concordance= 0.588 (se = 0.025 )
Likelihood ratio test= 15.11 on 3 df, p=0.002
Wald test = 14.36 on 3 df, p=0.002
Score (logrank) test = 14.43 on 3 df, p=0.002
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: 0.037093
Standard error............: 0.169988
Odds ratio (effect size)..: 1.038
Lower 95% CI..............: 0.744
Upper 95% CI..............: 1.448
T-value...................: 0.218206
P-value...................: 0.8272683
Sample size in model......: 1184
Number of events..........: 139
* Analyzing the effect of plaque proteins on [epstroke.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 1 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34055,0.00105) [ 0.00105,3.34055]
598 598
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 1184, number of events= 73
(1237 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 0.13646 1.14621 0.23508 0.580 0.5616
Age 0.03440 1.03500 0.01356 2.537 0.0112 *
Gendermale 0.06281 1.06483 0.25901 0.243 0.8084
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 1.146 0.8724 0.7230 1.817
Age 1.035 0.9662 1.0079 1.063
Gendermale 1.065 0.9391 0.6409 1.769
Concordance= 0.597 (se = 0.033 )
Likelihood ratio test= 7.12 on 3 df, p=0.07
Wald test = 6.89 on 3 df, p=0.08
Score (logrank) test = 6.92 on 3 df, p=0.07
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: 0.136457
Standard error............: 0.235075
Odds ratio (effect size)..: 1.146
Lower 95% CI..............: 0.723
Upper 95% CI..............: 1.817
T-value...................: 0.580483
P-value...................: 0.5615888
Sample size in model......: 1184
Number of events..........: 73
* Analyzing the effect of plaque proteins on [epcoronary.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 1 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34055,0.00105) [ 0.00105,3.34055]
598 598
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 1184, number of events= 91
(1237 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] -0.174454 0.839916 0.210481 -0.829 0.4072
Age 0.007135 1.007160 0.011848 0.602 0.5471
Gendermale 0.666235 1.946893 0.269214 2.475 0.0133 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 0.8399 1.1906 0.556 1.269
Age 1.0072 0.9929 0.984 1.031
Gendermale 1.9469 0.5136 1.149 3.300
Concordance= 0.577 (se = 0.03 )
Likelihood ratio test= 7.93 on 3 df, p=0.05
Wald test = 7.07 on 3 df, p=0.07
Score (logrank) test = 7.29 on 3 df, p=0.06
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: -0.174454
Standard error............: 0.210481
Odds ratio (effect size)..: 0.84
Lower 95% CI..............: 0.556
Upper 95% CI..............: 1.269
T-value...................: -0.828833
P-value...................: 0.4071991
Sample size in model......: 1184
Number of events..........: 91
* Analyzing the effect of plaque proteins on [epcvdeath.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 1 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34055,0.00105) [ 0.00105,3.34055]
598 598
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender, data = TEMP.DF)
n= 1184, number of events= 45
(1237 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] -0.11090 0.89502 0.29877 -0.371 0.7105
Age 0.08444 1.08811 0.01930 4.375 1.21e-05 ***
Gendermale 0.89483 2.44691 0.41200 2.172 0.0299 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 0.895 1.1173 0.4983 1.608
Age 1.088 0.9190 1.0477 1.130
Gendermale 2.447 0.4087 1.0913 5.487
Concordance= 0.71 (se = 0.036 )
Likelihood ratio test= 26.84 on 3 df, p=6e-06
Wald test = 23.16 on 3 df, p=4e-05
Score (logrank) test = 23.81 on 3 df, p=3e-05
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: -0.110904
Standard error............: 0.298774
Odds ratio (effect size)..: 0.895
Lower 95% CI..............: 0.498
Upper 95% CI..............: 1.608
T-value...................: -0.371199
P-value...................: 0.7104893
Sample size in model......: 1184
Number of events..........: 45 
cat("- Edit the column names...\n")- Edit the column names...colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")- Correct the variable types...COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")- Writing results to Excel-file...head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")- Removing intermediate files...#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
#rm(head.style)
MODEL 2
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
eptime = times[i]
ep = endpoints[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
#as.integer(TEMP.DF[,ep] == "Excluded")
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
# ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [years]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL2.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}* Analyzing the effect of plaque proteins on [epmajor.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 1 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34055,0.00105) [ 0.00105,3.34055]
598 598
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 1027, number of events= 115
(1394 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 1.327e-01 1.142e+00 1.898e-01 0.699 0.4843
Age 3.278e-02 1.033e+00 1.275e-02 2.572 0.0101 *
Gendermale 3.790e-01 1.461e+00 2.280e-01 1.662 0.0964 .
Hypertension.compositeno -4.193e-01 6.575e-01 3.564e-01 -1.177 0.2393
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -1.633e-02 9.838e-01 2.234e-01 -0.073 0.9417
SmokerStatusEx-smoker -5.028e-01 6.049e-01 2.095e-01 -2.400 0.0164 *
SmokerStatusNever smoked -8.142e-01 4.430e-01 3.415e-01 -2.385 0.0171 *
Med.Statin.LLDno 2.579e-01 1.294e+00 2.155e-01 1.197 0.2314
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 4.283e-01 1.535e+00 2.635e-01 1.626 0.1040
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.969e-02 9.805e-01 4.925e-03 -3.997 6.42e-05 ***
BMI 5.402e-02 1.056e+00 2.613e-02 2.067 0.0387 *
MedHx_CVDyes 5.306e-01 1.700e+00 2.220e-01 2.391 0.0168 *
stenose0-49% -1.560e+01 1.677e-07 2.462e+03 -0.006 0.9949
stenose50-70% -8.183e-01 4.412e-01 8.701e-01 -0.941 0.3470
stenose70-90% -2.441e-01 7.834e-01 7.298e-01 -0.335 0.7380
stenose90-99% -2.079e-01 8.123e-01 7.282e-01 -0.285 0.7753
stenose100% (Occlusion) -4.754e-02 9.536e-01 1.244e+00 -0.038 0.9695
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.529e+01 2.289e-07 2.920e+03 -0.005 0.9958
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 1.142e+00 8.757e-01 0.78722 1.6565
Age 1.033e+00 9.678e-01 1.00783 1.0595
Gendermale 1.461e+00 6.845e-01 0.93440 2.2840
Hypertension.compositeno 6.575e-01 1.521e+00 0.32699 1.3220
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.838e-01 1.016e+00 0.63498 1.5242
SmokerStatusEx-smoker 6.049e-01 1.653e+00 0.40120 0.9119
SmokerStatusNever smoked 4.430e-01 2.257e+00 0.22685 0.8650
Med.Statin.LLDno 1.294e+00 7.727e-01 0.84830 1.9746
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.535e+00 6.516e-01 0.91566 2.5720
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.805e-01 1.020e+00 0.97109 0.9900
BMI 1.056e+00 9.474e-01 1.00281 1.1110
MedHx_CVDyes 1.700e+00 5.882e-01 1.10031 2.6266
stenose0-49% 1.677e-07 5.962e+06 0.00000 Inf
stenose50-70% 4.412e-01 2.267e+00 0.08017 2.4278
stenose70-90% 7.834e-01 1.276e+00 0.18741 3.2747
stenose90-99% 8.123e-01 1.231e+00 0.19494 3.3848
stenose100% (Occlusion) 9.536e-01 1.049e+00 0.08333 10.9119
stenoseNA NA NA NA NA
stenose50-99% 2.289e-07 4.369e+06 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.697 (se = 0.023 )
Likelihood ratio test= 63.79 on 18 df, p=5e-07
Wald test = 58.83 on 18 df, p=3e-06
Score (logrank) test = 62.19 on 18 df, p=9e-07
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: 0.132746
Standard error............: 0.189795
Odds ratio (effect size)..: 1.142
Lower 95% CI..............: 0.787
Upper 95% CI..............: 1.657
T-value...................: 0.699416
P-value...................: 0.4842918
Sample size in model......: 1027
Number of events..........: 115
* Analyzing the effect of plaque proteins on [epstroke.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 1 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34055,0.00105) [ 0.00105,3.34055]
598 598
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 1027, number of events= 59
(1394 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 1.934e-01 1.213e+00 2.668e-01 0.725 0.4684
Age 4.267e-02 1.044e+00 1.756e-02 2.429 0.0151 *
Gendermale -4.383e-02 9.571e-01 2.992e-01 -0.146 0.8835
Hypertension.compositeno 8.567e-03 1.009e+00 4.178e-01 0.021 0.9836
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -2.315e-02 9.771e-01 3.168e-01 -0.073 0.9418
SmokerStatusEx-smoker -1.168e-01 8.898e-01 2.964e-01 -0.394 0.6936
SmokerStatusNever smoked -9.611e-01 3.825e-01 5.239e-01 -1.835 0.0666 .
Med.Statin.LLDno 3.743e-01 1.454e+00 2.926e-01 1.279 0.2008
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 3.817e-01 1.465e+00 3.714e-01 1.028 0.3040
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -4.331e-03 9.957e-01 6.992e-03 -0.619 0.5357
BMI 8.171e-02 1.085e+00 3.459e-02 2.362 0.0182 *
MedHx_CVDyes 3.652e-01 1.441e+00 2.940e-01 1.242 0.2142
stenose0-49% -1.523e+01 2.419e-07 3.388e+03 -0.004 0.9964
stenose50-70% -5.088e-01 6.012e-01 1.160e+00 -0.438 0.6610
stenose70-90% -2.645e-01 7.676e-01 1.029e+00 -0.257 0.7971
stenose90-99% -2.597e-01 7.713e-01 1.029e+00 -0.252 0.8008
stenose100% (Occlusion) 5.974e-01 1.817e+00 1.439e+00 0.415 0.6780
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.515e+01 2.629e-07 3.962e+03 -0.004 0.9969
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 1.213e+00 8.242e-01 0.71934 2.047
Age 1.044e+00 9.582e-01 1.00828 1.080
Gendermale 9.571e-01 1.045e+00 0.53242 1.721
Hypertension.compositeno 1.009e+00 9.915e-01 0.44469 2.288
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.771e-01 1.023e+00 0.52513 1.818
SmokerStatusEx-smoker 8.898e-01 1.124e+00 0.49772 1.591
SmokerStatusNever smoked 3.825e-01 2.615e+00 0.13699 1.068
Med.Statin.LLDno 1.454e+00 6.878e-01 0.81941 2.580
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.465e+00 6.827e-01 0.70740 3.033
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.957e-01 1.004e+00 0.98213 1.009
BMI 1.085e+00 9.215e-01 1.01402 1.161
MedHx_CVDyes 1.441e+00 6.940e-01 0.80970 2.564
stenose0-49% 2.419e-07 4.133e+06 0.00000 Inf
stenose50-70% 6.012e-01 1.663e+00 0.06185 5.844
stenose70-90% 7.676e-01 1.303e+00 0.10217 5.767
stenose90-99% 7.713e-01 1.297e+00 0.10256 5.800
stenose100% (Occlusion) 1.817e+00 5.503e-01 0.10830 30.495
stenoseNA NA NA NA NA
stenose50-99% 2.629e-07 3.804e+06 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.67 (se = 0.034 )
Likelihood ratio test= 23.02 on 18 df, p=0.2
Wald test = 20.97 on 18 df, p=0.3
Score (logrank) test = 22.15 on 18 df, p=0.2
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: 0.1934
Standard error............: 0.26675
Odds ratio (effect size)..: 1.213
Lower 95% CI..............: 0.719
Upper 95% CI..............: 2.047
T-value...................: 0.725021
P-value...................: 0.4684393
Sample size in model......: 1027
Number of events..........: 59
* Analyzing the effect of plaque proteins on [epcoronary.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 1 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34055,0.00105) [ 0.00105,3.34055]
598 598
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 1027, number of events= 78
(1394 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] -1.290e-01 8.790e-01 2.315e-01 -0.557 0.577222
Age -8.243e-04 9.992e-01 1.506e-02 -0.055 0.956352
Gendermale 8.625e-01 2.369e+00 3.029e-01 2.848 0.004404 **
Hypertension.compositeno -9.192e-01 3.989e-01 5.209e-01 -1.765 0.077619 .
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -1.045e-01 9.008e-01 2.759e-01 -0.379 0.704879
SmokerStatusEx-smoker -6.291e-01 5.331e-01 2.573e-01 -2.445 0.014496 *
SmokerStatusNever smoked -2.735e-01 7.607e-01 3.647e-01 -0.750 0.453241
Med.Statin.LLDno 1.147e-01 1.122e+00 2.730e-01 0.420 0.674465
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 3.664e-01 1.443e+00 3.348e-01 1.095 0.273710
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -2.059e-02 9.796e-01 5.940e-03 -3.466 0.000528 ***
BMI 1.350e-02 1.014e+00 3.290e-02 0.410 0.681500
MedHx_CVDyes 6.867e-01 1.987e+00 2.795e-01 2.457 0.014000 *
stenose0-49% -1.584e+01 1.320e-07 3.048e+03 -0.005 0.995853
stenose50-70% -1.713e+00 1.803e-01 1.418e+00 -1.208 0.227098
stenose70-90% -1.372e-01 8.718e-01 1.022e+00 -0.134 0.893141
stenose90-99% -1.918e-01 8.255e-01 1.022e+00 -0.188 0.851135
stenose100% (Occlusion) -1.540e+01 2.059e-07 2.452e+03 -0.006 0.994990
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% 8.142e-01 2.257e+00 1.429e+00 0.570 0.568977
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 8.790e-01 1.138e+00 0.55840 1.3835
Age 9.992e-01 1.001e+00 0.97011 1.0291
Gendermale 2.369e+00 4.221e-01 1.30849 4.2894
Hypertension.compositeno 3.989e-01 2.507e+00 0.14370 1.1071
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.008e-01 1.110e+00 0.52459 1.5468
SmokerStatusEx-smoker 5.331e-01 1.876e+00 0.32192 0.8827
SmokerStatusNever smoked 7.607e-01 1.315e+00 0.37224 1.5546
Med.Statin.LLDno 1.122e+00 8.917e-01 0.65677 1.9151
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.443e+00 6.932e-01 0.74849 2.7802
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.796e-01 1.021e+00 0.96828 0.9911
BMI 1.014e+00 9.866e-01 0.95030 1.0811
MedHx_CVDyes 1.987e+00 5.032e-01 1.14909 3.4365
stenose0-49% 1.320e-07 7.576e+06 0.00000 Inf
stenose50-70% 1.803e-01 5.547e+00 0.01118 2.9061
stenose70-90% 8.718e-01 1.147e+00 0.11772 6.4559
stenose90-99% 8.255e-01 1.211e+00 0.11137 6.1183
stenose100% (Occlusion) 2.059e-07 4.857e+06 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 2.257e+00 4.430e-01 0.13703 37.1835
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.726 (se = 0.028 )
Likelihood ratio test= 49.9 on 18 df, p=8e-05
Wald test = 43.46 on 18 df, p=7e-04
Score (logrank) test = 47.19 on 18 df, p=2e-04
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: -0.129025
Standard error............: 0.231458
Odds ratio (effect size)..: 0.879
Lower 95% CI..............: 0.558
Upper 95% CI..............: 1.384
T-value...................: -0.557447
P-value...................: 0.577222
Sample size in model......: 1027
Number of events..........: 78
* Analyzing the effect of plaque proteins on [epcvdeath.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 1 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34055,0.00105) [ 0.00105,3.34055]
598 598
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 1027, number of events= 33
(1394 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] -2.044e-01 8.152e-01 3.583e-01 -0.570 0.56836
Age 6.640e-02 1.069e+00 2.655e-02 2.501 0.01238 *
Gendermale 1.258e+00 3.520e+00 5.565e-01 2.261 0.02373 *
Hypertension.compositeno -1.774e+01 1.978e-08 4.017e+03 -0.004 0.99648
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -3.835e-02 9.624e-01 4.261e-01 -0.090 0.92829
SmokerStatusEx-smoker -5.672e-01 5.671e-01 4.021e-01 -1.411 0.15834
SmokerStatusNever smoked -4.302e-01 6.504e-01 6.158e-01 -0.699 0.48479
Med.Statin.LLDno 8.681e-02 1.091e+00 4.156e-01 0.209 0.83454
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.130e+00 3.096e+00 4.161e-01 2.716 0.00661 **
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -3.395e-02 9.666e-01 9.369e-03 -3.624 0.00029 ***
BMI 8.410e-02 1.088e+00 5.118e-02 1.643 0.10031
MedHx_CVDyes 7.683e-01 2.156e+00 4.605e-01 1.668 0.09528 .
stenose0-49% -2.031e+01 1.509e-09 2.713e+04 -0.001 0.99940
stenose50-70% -1.041e+00 3.531e-01 1.236e+00 -0.842 0.39970
stenose70-90% -1.480e+00 2.276e-01 1.069e+00 -1.385 0.16609
stenose90-99% -1.069e+00 3.434e-01 1.054e+00 -1.014 0.31055
stenose100% (Occlusion) -1.957e+01 3.154e-09 1.974e+04 -0.001 0.99921
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.939e+01 3.802e-09 3.407e+04 -0.001 0.99955
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34055] 8.152e-01 1.227e+00 0.40392 1.6451
Age 1.069e+00 9.358e-01 1.01447 1.1257
Gendermale 3.520e+00 2.841e-01 1.18264 10.4751
Hypertension.compositeno 1.978e-08 5.054e+07 0.00000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.624e-01 1.039e+00 0.41748 2.2185
SmokerStatusEx-smoker 5.671e-01 1.763e+00 0.25789 1.2471
SmokerStatusNever smoked 6.504e-01 1.538e+00 0.19453 2.1744
Med.Statin.LLDno 1.091e+00 9.168e-01 0.48298 2.4631
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 3.096e+00 3.230e-01 1.36967 6.9988
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.666e-01 1.035e+00 0.94903 0.9845
BMI 1.088e+00 9.193e-01 0.98393 1.2025
MedHx_CVDyes 2.156e+00 4.638e-01 0.87427 5.3170
stenose0-49% 1.509e-09 6.625e+08 0.00000 Inf
stenose50-70% 3.531e-01 2.832e+00 0.03132 3.9813
stenose70-90% 2.276e-01 4.394e+00 0.02802 1.8490
stenose90-99% 3.434e-01 2.912e+00 0.04351 2.7102
stenose100% (Occlusion) 3.154e-09 3.170e+08 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 3.802e-09 2.630e+08 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.839 (se = 0.031 )
Likelihood ratio test= 60.15 on 18 df, p=2e-06
Wald test = 20.83 on 18 df, p=0.3
Score (logrank) test = 56.03 on 18 df, p=9e-06
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: -0.204374
Standard error............: 0.358254
Odds ratio (effect size)..: 0.815
Lower 95% CI..............: 0.404
Upper 95% CI..............: 1.645
T-value...................: -0.570471
P-value...................: 0.568358
Sample size in model......: 1027
Number of events..........: 33 
cat("- Edit the column names...\n")- Edit the column names...colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")- Correct the variable types...COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")- Writing results to Excel-file...head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")- Removing intermediate files...rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
rm(head.style)object 'head.style' not found30-days follow-up
MODEL 1
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
eptime = times30[i]
ep = endpoints30[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
ylim = c(0.75, 1),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 1 (Simple model)
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
# ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [days]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL1.30days.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}
cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.30days.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
#rm(head.style)
MODEL 2
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
eptime = times30[i]
ep = endpoints30[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
#as.integer(TEMP.DF[,ep] == "Excluded")
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
ylim = c(0.75, 1),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
# ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [days]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL2.30days.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}
cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.30days.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
rm(head.style)
90-days follow-up
MODEL 1
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
eptime = times90[i]
ep = endpoints90[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
ylim = c(0.75, 1),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 1 (Simple model)
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
# ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [days]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL1.90days.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}
cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.90days.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
#rm(head.style)
MODEL 2
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
eptime = times90[i]
ep = endpoints90[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
#as.integer(TEMP.DF[,ep] == "Excluded")
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
ylim = c(0.75, 1),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
# ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [days]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL2.90days.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}
cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.90days.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
rm(head.style)
Correlations
All biomarkers
We correlated serum and plaque levels of the biomarkers.
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools))
install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")Skipping install of 'ggcorrplot' from a github remote, the SHA1 (c46b4cce) has not changed since last install.
Use `force = TRUE` to force installationlibrary(ggcorrplot)
# Creating matrix - inverse-rank transformation
# --------------------------------
# AEDB.CEA.temp <- subset(AEDB.CEA,
# select = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank",
# TRAITS.BIN, TRAITS.CON.RANK)
# )
# AEDB.CEA.temp <- subset(AEDB.CEA,
# select = c("MCP1_rank", "MCP1_pg_ug_2015_rank",
# TRAITS.BIN, TRAITS.CON.RANK)
# )
AEDB.CEA.temp <- subset(AEDB.CEA,
select = c("MCP1_pg_ug_2015_rank",
TRAITS.BIN, TRAITS.CON.RANK)
)
AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
str(AEDB.CEA.temp)tibble [2,421 × 8] (S3: tbl_df/tbl/data.frame)
$ MCP1_pg_ug_2015_rank: num [1:2421] NA 0.937 2.158 1.209 1.996 ...
$ CalcificationPlaque : num [1:2421] 1 1 1 1 1 2 2 1 2 1 ...
$ CollagenPlaque : num [1:2421] 1 2 2 2 2 1 NA 2 2 2 ...
$ Fat10Perc : num [1:2421] 2 2 2 2 2 2 2 2 2 2 ...
$ IPH : num [1:2421] 2 2 2 2 1 2 2 2 2 2 ...
$ Macrophages_rank : num [1:2421] 1.388 1.12 1.365 0.721 0.396 ...
$ SMC_rank : num [1:2421] 1.67023 1.13089 0.00295 1.42623 1.2689 ...
$ VesselDensity_rank : num [1:2421] -0.529 -0.977 -0.774 0.716 1.099 ...AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)
corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK,
use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
method = "spearman"), 3)
# corr_biomarkers.rank
corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")Cannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with ties# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers.rank,
method = "square",
type = "lower",
title = "Cross biomarker correlations",
show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
show.diag = TRUE,
hc.order = FALSE,
lab = FALSE,
digits = 3,
# p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
colors = c("#1290D9", "#FFFFFF", "#E55738"))
# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
ut <- upper.tri(cormat)
data.frame(
biomarker_row = rownames(cormat)[row(cormat)[ut]],
biomarker_column = rownames(cormat)[col(cormat)[ut]],
spearman_cor =(cormat)[ut],
pval = pmat[ut]
)
}
corr_biomarkers.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank))
DT::datatable(corr_biomarkers.rank.df)
# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall",
"spearman"), ...)
{
x = checkData(R, method = "matrix")
if (missing(method))
method = method[1]
cormeth <- method
panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs",
method = cormeth, cex.cor, ...) {
usr <- par("usr")
on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
r <- cor(x, y, use = use, method = method)
txt <- format(c(r, 0.123456789), digits = digits)[1]
txt <- paste(prefix, txt, sep = "")
if (missing(cex.cor))
cex <- 0.8/strwidth(txt)
test <- cor.test(as.numeric(x), as.numeric(y), method = method)
Signif <- symnum(test$p.value, corr = FALSE, na = FALSE,
cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***",
"**", "*", ".", " "))
text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
text(0.8, 0.8, Signif, cex = cex, col = 2)
}
f <- function(t) {
dnorm(t, mean = mean(x), sd = sd.xts(x))
}
dotargs <- list(...)
dotargs$method <- NULL
rm(method)
hist.panel = function(x, ... = NULL) {
par(new = TRUE)
hist(x, col = "#1290D9", probability = TRUE, axes = FALSE,
# hist(x, col = "light gray", probability = TRUE, axes = FALSE,
main = "", breaks = "FD")
lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
rug(x)
}
if (histogram)
pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor,
diag.panel = hist.panel, ...)
else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}
chart.Correlation.new(AEDB.CEA.matrix.RANK, method = "spearman", histogram = TRUE, pch = 3)
# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")
# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables
library(GGally)
# From the help page:
# ggpairs(AEDB.CEA,
# columns = c("MCP1_rank", "MCP1_pg_ug_2015_rank", TRAITS.BIN, TRAITS.CON.RANK),
# columnLabels = c("MCP1 (serum)", "MCP1",
# "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density"),
# method = c("spearman"),
# # ggplot2::aes(colour = Gender),
# progress = FALSE)
ggpairs(AEDB.CEA,
columns = c("MCP1_pg_ug_2015_rank", TRAITS.BIN, TRAITS.CON.RANK),
columnLabels = c("MCP1",
"Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density"),
method = c("spearman"),
# ggplot2::aes(colour = Gender),
progress = FALSE)Extra arguments: 'method' are being ignored. If these are meant to be aesthetics, submit them using the 'mapping' variable within ggpairs with ggplot2::aes or ggplot2::aes_string.
Circulating MCP1
Finally, we explored in a sub-sample, where circulating MCP-1 levels are available, the following:
- A correlation between MCP-1 levels in the plaque and circulating MCP-1 levels
- Associations of circulating MCP-1 levels with plaque vulnerability characteristics
- Associations of circulating MCP-1 levels with the status of the plaque in terms of presence of symptoms (symptomatic vs. asymptomatic)
- Associations of circulating MCP-1 levels with the primary composite endpoint of secondary cardiovascular events.
NOT AVAILABLE YET
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools))
install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")
library(ggcorrplot)
# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA,
select = c("MCP1_rank",
TRAITS.BIN, TRAITS.CON.RANK,
"Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
)
AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
AEDB.CEA.temp$Symptoms.5G <- as.numeric(AEDB.CEA.temp$Symptoms.5G)
AEDB.CEA.temp$AsymptSympt <- as.numeric(AEDB.CEA.temp$AsymptSympt)
AEDB.CEA.temp$EP_major <- as.numeric(AEDB.CEA.temp$EP_major)
AEDB.CEA.temp$EP_composite <- as.numeric(AEDB.CEA.temp$EP_composite)
# str(AEDB.CEA.temp)
AEDB.CEA.matrix.serum.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)
corr_biomarkers_serum.rank <- round(cor(AEDB.CEA.matrix.serum.RANK,
use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
method = "spearman"), 3)
# corr_biomarkers.rank
corr_biomarkers_serum_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.serum.RANK, use = "pairwise.complete.obs", method = "spearman")
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers_serum.rank,
method = "square",
type = "lower",
title = "Cross biomarker correlations",
show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
show.diag = TRUE,
hc.order = FALSE,
lab = FALSE,
digits = 3,
# p.mat = corr_biomarkers_serum_p.rank, sig.level = 0.05,
colors = c("#1290D9", "#FFFFFF", "#E55738"))
# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
ut <- upper.tri(cormat)
data.frame(
biomarker_row = rownames(cormat)[row(cormat)[ut]],
biomarker_column = rownames(cormat)[col(cormat)[ut]],
spearman_cor =(cormat)[ut],
pval = pmat[ut]
)
}
corr_biomarkers_serum.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers_serum.rank, corr_biomarkers_serum_p.rank))
DT::datatable(corr_biomarkers_serum.rank.df)
# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall",
"spearman"), ...)
{
x = checkData(R, method = "matrix")
if (missing(method))
method = method[1]
cormeth <- method
panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs",
method = cormeth, cex.cor, ...) {
usr <- par("usr")
on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
r <- cor(x, y, use = use, method = method)
txt <- format(c(r, 0.123456789), digits = digits)[1]
txt <- paste(prefix, txt, sep = "")
if (missing(cex.cor))
cex <- 0.8/strwidth(txt)
test <- cor.test(as.numeric(x), as.numeric(y), method = method)
Signif <- symnum(test$p.value, corr = FALSE, na = FALSE,
cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***",
"**", "*", ".", " "))
text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
text(0.8, 0.8, Signif, cex = cex, col = 2)
}
f <- function(t) {
dnorm(t, mean = mean(x), sd = sd.xts(x))
}
dotargs <- list(...)
dotargs$method <- NULL
rm(method)
hist.panel = function(x, ... = NULL) {
par(new = TRUE)
hist(x, col = "#1290D9", probability = TRUE, axes = FALSE,
# hist(x, col = "light gray", probability = TRUE, axes = FALSE,
main = "", breaks = "FD")
lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
rug(x)
}
if (histogram)
pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor,
diag.panel = hist.panel, ...)
else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}
chart.Correlation.new(AEDB.CEA.matrix.serum.RANK, method = "spearman", histogram = TRUE, pch = 3)
# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")
# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables
library(GGally)
# From the help page:
ggpairs(AEDB.CEA,
columns = c("MCP1_rank", TRAITS.BIN, TRAITS.CON.RANK, "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite"),
columnLabels = c("MCP1 (serum)",
"Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density",
"Symptoms", "Symptoms (grouped)", "MACE", "Composite"),
method = c("spearman"),
# ggplot2::aes(colour = Gender),
progress = FALSE) Additional figures
Age and sex
We want to create per-age-group figures.
- Box and Whisker plot for MCP-1 plaque levels by sex and age group (<55, 55-64, 65-74, 75-84, 85+)
- Box and Whisker plot for MCP-1 serum levels by sex and age group (<55, 55-64, 65-74, 75-84, 85+)
- Scatter plot of the correlation and regression line between MCP-1 levels in plaque (y axis) and serum (x axis).
library(dplyr)
AEDB.CEA <- AEDB.CEA %>% mutate(AgeGroup = factor(case_when(Age < 55 ~ "<55",
Age >= 55 & Age <= 64 ~ "55-64",
Age >= 65 & Age <= 74 ~ "65-74",
Age >= 75 & Age <= 84 ~ "75-84",
Age >= 85 ~ "85+")))
AEDB.CEA <- AEDB.CEA %>% mutate(AgeGroupSex = factor(case_when(Age < 55 & Gender == "male" ~ "<55 males" ,
Age >= 55 & Age <= 64 & Gender == "male"~ "55-64 males",
Age >= 65 & Age <= 74 & Gender == "male"~ "65-74 males",
Age >= 75 & Age <= 84 & Gender == "male"~ "75-84 males",
Age >= 85 & Gender == "male"~ "85+ males",
Age < 55 & Gender == "female" ~ "<55 females" ,
Age >= 55 & Age <= 64 & Gender == "female"~ "55-64 females ",
Age >= 65 & Age <= 74 & Gender == "female"~ "65-74 females",
Age >= 75 & Age <= 84 & Gender == "female"~ "75-84 females",
Age >= 85 & Gender == "female"~ "85+ females")))
table(AEDB.CEA$AgeGroup, AEDB.CEA$Gender)
female male
<55 45 98
55-64 193 410
65-74 264 687
75-84 202 438
85+ 34 50table(AEDB.CEA$AgeGroupSex)
<55 females <55 males 55-64 females 55-64 males 65-74 females 65-74 males 75-84 females 75-84 males 85+ females
45 98 193 410 264 687 202 438 34
85+ males
50 Now we can draw some graphs of serum/plaque MCP1 levels per sex and age group.
Inverse-rank transformed data
MCP1 plaque levels
# ?ggpubr::ggboxplot()
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("Gender"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Age groups (years) per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")MCP1 serum levels
NOT AVAILABLE YET
# compare_means(MCP1_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("Gender"),
y = "MCP1_rank",
xlab = "Gender",
ylab = "MCP1 serum [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
# compare_means(MCP1_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1_rank",
xlab = "Age groups (years) per gender",
ylab = "MCP1 serum [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
Raw data
Simalarly but now for the raw data as median ± interquartile range.
MCP1 plaque levels
# ?ggpubr::ggboxplot()
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("Gender"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Age groups (years) per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")MCP1 serum levels
NOT AVAILABLE YET
ggpubr::ggboxplot(AEDB.CEA,
x = c("Gender"),
y = "MCP1",
xlab = "Gender",
ylab = "MCP1 serum [pg/mL]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
# add = "median_iqr")
add = c("median_iqr", "jitter"))
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1",
xlab = "Age groups (years) per gender",
ylab = "MCP1 serum [pg/mL]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
# add = "median_iqr")
add = c("median_iqr", "jitter"))Circulating vs. plaque MCP1 levels
We will also make a nice correlation plot between serum and plaque MCP1 levels.
NOT AVAILABLE YET
ggpubr::ggscatter(AEDB.CEA,
x = "MCP1_pg_ug_2015",
y = "MCP1",
xlab = "MCP1 plaque [pg/ug]",
ylab = "MCP1 serum [pg/mL]",
add = "reg.line", add.params = list(color = "#1290D9"),
conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method = "spearman"), cor.coef.coord = c(8,750))
ggpubr::ggscatter(AEDB.CEA,
x = "MCP1_pg_ug_2015_rank",
y = "MCP1_rank",
xlab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
ylab = "MCP1 serum [pg/mL]\n(inverse-rank transformation)",
add = "reg.line", add.params = list(color = "#1290D9"),
conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method = "spearman"), cor.coef.coord = c(2,3))
Plaque vs. plaque MCP1 levels
We will also make a nice correlation plot between the two experiments of plaque MCP1 levels.
AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
summary(AEDB.CEA$MCP1)
summary(AEDB.CEA$MCP1_pg_ug_2015)
ggpubr::ggscatter(AEDB.CEA,
x = "MCP1",
y = "MCP1_pg_ug_2015",
xlab = "MCP1 plaque [pg/mL] (exp. no. 1)",
ylab = "MCP1 plaque [pg/ug] (exp. no. 2)",
add = "reg.line", add.params = list(color = "#1290D9"),
conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method = "spearman"))
ggpubr::ggscatter(AEDB.CEA,
x = "MCP1_rank",
y = "MCP1_pg_ug_2015_rank",
xlab = "MCP1 plaque [pg/mL]\n(INRT, exp. no. 1)",
ylab = "MCP1 plaque [pg/ug]\n(INRT, exp. no. 2)",
add = "reg.line", add.params = list(color = "#1290D9"),
conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method = "spearman"))
Symptoms
We want to create per-symptom figures.
library(dplyr)
table(AEDB.CEA$AgeGroup, AEDB.CEA$AsymptSympt2G)
Asymptomatic Symptomatic
<55 24 119
55-64 76 527
65-74 124 827
75-84 43 597
85+ 3 81table(AEDB.CEA$Gender, AEDB.CEA$AsymptSympt2G)
Asymptomatic Symptomatic
female 64 674
male 206 1477table(AEDB.CEA$AsymptSympt2G)
Asymptomatic Symptomatic
270 2151 Now we can draw some graphs of serum/plaque MCP1 levels per symptom group.
# ?ggpubr::ggboxplot()
my_comparisons <- list(c("Asymptomatic", "Symptomatic"))
p1 <- ggpubr::ggboxplot(AEDB.CEA,
x = "AsymptSympt2G", y = "MCP1_pg_ug_2015_rank",
title = "MCP1 plaque [pg/ug] levels per symptom",
xlab = "Symptoms",
ylab = "MCP1 plaque [pg/ug]\n inverse-rank transformation",
color = "AsymptSympt2G",
palette = c(uithof_color[16], uithof_color[23]),
add = "dotplot", # Add dotplot
add.params = list(binwidth = 0.1, dotsize = 0.3)
) +
stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
p1 <- ggpubr::ggboxplot(AEDB.CEA,
x = "AsymptSympt2G", y = "MCP1_rank",
title = "MCP1 serum [pg/mL] levels per symptom",
xlab = "Symptoms",
ylab = "MCP1 serum [pg/mL]\n inverse-rank transformation",
color = "AsymptSympt2G",
palette = c(uithof_color[16], uithof_color[23]),
add = "dotplot", # Add dotplot
add.params = list(binwidth = 0.1, dotsize = 0.3)
) +
stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
rm(p1)Forest plots
We would also like to visualize the multivariable analyses results.
library(ggplot2)
library(openxlsx)
model1_mcp1 <- read.xlsx(paste0(OUT_loc, "/", Today, ".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"))
model2_mcp1 <- read.xlsx(paste0(OUT_loc, "/", Today, ".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"))
model1_mcp1$model <- "univariate"
model2_mcp1$model <- "multivariate"
models_mcp1 <- rbind(model1_mcp1, model2_mcp1)
models_mcp1NAdat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"),
levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
cen = c(models_mcp1$OR[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]),
low = c(models_mcp1$low95CI[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]),
high = c(models_mcp1$up95CI[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]))
fp <- ggplot(data=dat, aes(x=group, y=cen, ymin=low, ymax=high)) +
geom_pointrange() +
geom_hline(yintercept=1, lty=2) + # add a dotted line at x=1 after flip
coord_flip() + # flip coordinates (puts labels on y axis)
xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
theme(text = element_text(size=14)) +
ggtitle("Plaque MCP-1 levels (1 SD increment)") +
theme_minimal() # use a white background
print(fp)
rm(fp)dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"),
levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
cen = c(models_mcp1$OR[models_mcp1$Predictor=="MCP1_rank"]),
low = c(models_mcp1$low95CI[models_mcp1$Predictor=="MCP1_rank"]),
high = c(models_mcp1$up95CI[models_mcp1$Predictor=="MCP1_rank"]))
fp <- ggplot(data=dat, aes(x=group, y=cen, ymin=low, ymax=high)) +
geom_pointrange() +
geom_hline(yintercept=1, lty=2) + # add a dotted line at x=1 after flip
coord_flip() + # flip coordinates (puts labels on y axis)
xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
theme(text = element_text(size=14)) +
ggtitle("Serum MCP-1 levels (1 SD increment)") +
theme_minimal() # use a white background
print(fp)
rm(fp)MCP1 vs. cytokines plaque levels correlations
We will plot the correlations of other cytokine plaque levels to the MCP1 plaque levels. These include:
- IL2
- IL4
- IL5
- IL6
- IL8
- IL9
- IL10
- IL12
- IL13
- IL21
- INFG
- TNFA
- MIF
- MCP1
- MIP1a
- RANTES
- MIG
- IP10
- Eotaxin1
- TARC
- PARC
- MDC
- OPG
- sICAM1
- VEGFA
- TGFB
In addition we will look at three metalloproteinases which were measured using an activity assay.
- MMP2
- MMP8
- MMP9
The proteins were measured using FACS and LUMINEX. Given the different platforms used (FACS vs. LUMINEX), we will inverse rank-normalize these variables as well to scale them to the same scale as the MCP1 plaque levels.
We will set the measurements that yielded ‘0’ to NA, as it is unlikely that any protein ever has exactly 0 copies. The ‘0’ yielded during the experiment are due to the limits of the detection.
Prepare data
cytokines <- c("IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", "IL13", "IL21",
"INFG", "TNFA", "MIF", "MCP1", "MIP1a", "RANTES", "MIG", "IP10", "Eotaxin1",
"TARC", "PARC", "MDC", "OPG", "sICAM1", "VEGFA", "TGFB")
metalloproteinases <- c("MMP2", "MMP8", "MMP9")
# fix names
names(AEDB.CEA)[names(AEDB.CEA) == "VEFGA"] <- "VEGFA"
proteins_of_interest <- c(cytokines, metalloproteinases)
proteins_of_interest_rank = unlist(lapply(proteins_of_interest, paste0, "_rank"))
# make variables numerics()
AEDB.CEA <- AEDB.CEA %>%
mutate_each(funs(as.numeric), proteins_of_interest)funs() is soft deprecated as of dplyr 0.8.0
Please use a list of either functions or lambdas:
# Simple named list:
list(mean = mean, median = median)
# Auto named with `tibble::lst()`:
tibble::lst(mean, median)
# Using lambdas
list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
[90mThis warning is displayed once per session.[39mNote: Using an external vector in selections is ambiguous.
[34mℹ[39m Use `all_of(proteins_of_interest)` instead of `proteins_of_interest` to silence this message.
[34mℹ[39m See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
[90mThis message is displayed once per session.[39m
for(PROTEIN in 1:length(proteins_of_interest)){
# UCORBIOGSAqc$Z <- NULL
var.temp.rank = proteins_of_interest_rank[PROTEIN]
var.temp = proteins_of_interest[PROTEIN]
cat(paste0("\nSelecting ", var.temp, " and standardising: ", var.temp.rank,".\n"))
cat(paste0("* changing ", var.temp, " to numeric.\n"))
# AEDB.CEA <- AEDB.CEA %>% mutate(AEDB.CEA[,var.temp] == replace(AEDB.CEA[,var.temp], AEDB.CEA[,var.temp]==0, NA))
AEDB.CEA[,var.temp][AEDB.CEA[,var.temp]==0.000000]=NA
cat(paste0("* standardising ", var.temp,
" (mean: ",round(mean(!is.na(AEDB.CEA[,var.temp])), digits = 6),
", n = ",sum(!is.na(AEDB.CEA[,var.temp])),").\n"))
AEDB.CEA <- AEDB.CEA %>%
mutate_at(vars(var.temp),
# list(Z = ~ (AEDB.CEA[,var.temp] - mean(AEDB.CEA[,var.temp], na.rm = TRUE))/sd(AEDB.CEA[,var.temp], na.rm = TRUE))
list(RANK = ~ qnorm((rank(AEDB.CEA[,var.temp], na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA[,var.temp]))))
)
# str(UCORBIOGSAqc$Z)
cat(paste0("* renaming RANK to ", var.temp.rank,".\n"))
AEDB.CEA[,var.temp.rank] <- NULL
names(AEDB.CEA)[names(AEDB.CEA) == "RANK"] <- var.temp.rank
}
Selecting IL2 and standardising: IL2_rank.
* changing IL2 to numeric.
* standardising IL2 (mean: 0.180091, n = 436).Note: Using an external vector in selections is ambiguous.
[34mℹ[39m Use `all_of(var.temp)` instead of `var.temp` to silence this message.
[34mℹ[39m See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
[90mThis message is displayed once per session.[39m* renaming RANK to IL2_rank.
Selecting IL4 and standardising: IL4_rank.
* changing IL4 to numeric.
* standardising IL4 (mean: 0.167699, n = 406).
* renaming RANK to IL4_rank.
Selecting IL5 and standardising: IL5_rank.
* changing IL5 to numeric.
* standardising IL5 (mean: 0.178439, n = 432).
* renaming RANK to IL5_rank.
Selecting IL6 and standardising: IL6_rank.
* changing IL6 to numeric.
* standardising IL6 (mean: 0.188352, n = 456).
* renaming RANK to IL6_rank.
Selecting IL8 and standardising: IL8_rank.
* changing IL8 to numeric.
* standardising IL8 (mean: 0.182156, n = 441).
* renaming RANK to IL8_rank.
Selecting IL9 and standardising: IL9_rank.
* changing IL9 to numeric.
* standardising IL9 (mean: 0.206526, n = 500).
* renaming RANK to IL9_rank.
Selecting IL10 and standardising: IL10_rank.
* changing IL10 to numeric.
* standardising IL10 (mean: 0.159025, n = 385).
* renaming RANK to IL10_rank.
Selecting IL12 and standardising: IL12_rank.
* changing IL12 to numeric.
* standardising IL12 (mean: 0.168112, n = 407).
* renaming RANK to IL12_rank.
Selecting IL13 and standardising: IL13_rank.
* changing IL13 to numeric.
* standardising IL13 (mean: 0.232962, n = 564).
* renaming RANK to IL13_rank.
Selecting IL21 and standardising: IL21_rank.
* changing IL21 to numeric.
* standardising IL21 (mean: 0.233375, n = 565).
* renaming RANK to IL21_rank.
Selecting INFG and standardising: INFG_rank.
* changing INFG to numeric.
* standardising INFG (mean: 0.179265, n = 434).
* renaming RANK to INFG_rank.
Selecting TNFA and standardising: TNFA_rank.
* changing TNFA to numeric.
* standardising TNFA (mean: 0.163569, n = 396).
* renaming RANK to TNFA_rank.
Selecting MIF and standardising: MIF_rank.
* changing MIF to numeric.
* standardising MIF (mean: 0.233375, n = 565).
* renaming RANK to MIF_rank.
Selecting MCP1 and standardising: MCP1_rank.
* changing MCP1 to numeric.
* standardising MCP1 (mean: 0.229657, n = 556).
* renaming RANK to MCP1_rank.
Selecting MIP1a and standardising: MIP1a_rank.
* changing MIP1a to numeric.
* standardising MIP1a (mean: 0.211896, n = 513).
* renaming RANK to MIP1a_rank.
Selecting RANTES and standardising: RANTES_rank.
* changing RANTES to numeric.
* standardising RANTES (mean: 0.228831, n = 554).
* renaming RANK to RANTES_rank.
Selecting MIG and standardising: MIG_rank.
* changing MIG to numeric.
* standardising MIG (mean: 0.227179, n = 550).
* renaming RANK to MIG_rank.
Selecting IP10 and standardising: IP10_rank.
* changing IP10 to numeric.
* standardising IP10 (mean: 0.206113, n = 499).
* renaming RANK to IP10_rank.
Selecting Eotaxin1 and standardising: Eotaxin1_rank.
* changing Eotaxin1 to numeric.
* standardising Eotaxin1 (mean: 0.233375, n = 565).
* renaming RANK to Eotaxin1_rank.
Selecting TARC and standardising: TARC_rank.
* changing TARC to numeric.
* standardising TARC (mean: 0.200743, n = 486).
* renaming RANK to TARC_rank.
Selecting PARC and standardising: PARC_rank.
* changing PARC to numeric.
* standardising PARC (mean: 0.233375, n = 565).
* renaming RANK to PARC_rank.
Selecting MDC and standardising: MDC_rank.
* changing MDC to numeric.
* standardising MDC (mean: 0.209831, n = 508).
* renaming RANK to MDC_rank.
Selecting OPG and standardising: OPG_rank.
* changing OPG to numeric.
* standardising OPG (mean: 0.232962, n = 564).
* renaming RANK to OPG_rank.
Selecting sICAM1 and standardising: sICAM1_rank.
* changing sICAM1 to numeric.
* standardising sICAM1 (mean: 0.233375, n = 565).
* renaming RANK to sICAM1_rank.
Selecting VEGFA and standardising: VEGFA_rank.
* changing VEGFA to numeric.
* standardising VEGFA (mean: 0.20157, n = 488).
* renaming RANK to VEGFA_rank.
Selecting TGFB and standardising: TGFB_rank.
* changing TGFB to numeric.
* standardising TGFB (mean: 0.23007, n = 557).
* renaming RANK to TGFB_rank.
Selecting MMP2 and standardising: MMP2_rank.
* changing MMP2 to numeric.
* standardising MMP2 (mean: 0.232135, n = 562).
* renaming RANK to MMP2_rank.
Selecting MMP8 and standardising: MMP8_rank.
* changing MMP8 to numeric.
* standardising MMP8 (mean: 0.232135, n = 562).
* renaming RANK to MMP8_rank.
Selecting MMP9 and standardising: MMP9_rank.
* changing MMP9 to numeric.
* standardising MMP9 (mean: 0.231722, n = 561).
* renaming RANK to MMP9_rank.# rm(var.temp, var.temp.rank)
Visualize transformations
We will just visualize these transformations.
proteins_of_interest_rank_mcp1 <- c("MCP1_pg_ug_2015_rank", proteins_of_interest_rank)
proteins_of_interest_mcp1 <- c("MCP1_pg_ug_2015", proteins_of_interest)
for(PROTEIN in proteins_of_interest_mcp1){
cat(paste0("Plotting protein ", PROTEIN, ".\n"))
p1 <- ggpubr::gghistogram(AEDB.CEA, PROTEIN,
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = paste0(PROTEIN, " plaque levels"),
xlab = "",
ggtheme = theme_minimal())
print(p1)
}Plotting protein MCP1_pg_ug_2015.Using `bins = 30` by default. Pick better value with the argument `bins`.Plotting protein IL2.
Plotting protein IL4.
Plotting protein IL5.
Plotting protein IL6.
Plotting protein IL8.
Plotting protein IL9.
Plotting protein IL10.
Plotting protein IL12.
Plotting protein IL13.
Plotting protein IL21.
Plotting protein INFG.
Plotting protein TNFA.
Plotting protein MIF.
Plotting protein MCP1.
Plotting protein MIP1a.
Plotting protein RANTES.
Plotting protein MIG.
Plotting protein IP10.
Plotting protein Eotaxin1.
Plotting protein TARC.
Plotting protein PARC.
Plotting protein MDC.
Plotting protein OPG.
Plotting protein sICAM1.
Plotting protein VEGFA.
Plotting protein TGFB.
Plotting protein MMP2.
Plotting protein MMP8.
Plotting protein MMP9.
for(PROTEIN in proteins_of_interest_rank_mcp1){
cat(paste0("Plotting protein ", PROTEIN, ".\n"))
p1 <- ggpubr::gghistogram(AEDB.CEA, PROTEIN,
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = paste0(PROTEIN, " plaque levels"),
xlab = "inverse-normal transformation",
ggtheme = theme_minimal())
print(p1)
}Plotting protein MCP1_pg_ug_2015_rank.Using `bins = 30` by default. Pick better value with the argument `bins`.Plotting protein IL2_rank.
Plotting protein IL4_rank.
Plotting protein IL5_rank.
Plotting protein IL6_rank.
Plotting protein IL8_rank.
Plotting protein IL9_rank.
Plotting protein IL10_rank.
Plotting protein IL12_rank.
Plotting protein IL13_rank.
Plotting protein IL21_rank.
Plotting protein INFG_rank.
Plotting protein TNFA_rank.
Plotting protein MIF_rank.
Plotting protein MCP1_rank.
Plotting protein MIP1a_rank.
Plotting protein RANTES_rank.
Plotting protein MIG_rank.
Plotting protein IP10_rank.
Plotting protein Eotaxin1_rank.
Plotting protein TARC_rank.
Plotting protein PARC_rank.
Plotting protein MDC_rank.
Plotting protein OPG_rank.
Plotting protein sICAM1_rank.
Plotting protein VEGFA_rank.
Plotting protein TGFB_rank.
Plotting protein MMP2_rank.
Plotting protein MMP8_rank.
Plotting protein MMP9_rank.
NACorrelations
Here we calculate correlations between MCP1_pg_ug_2015 and 28 other cytokines (including MCP1 as measured in experiment 1. We use Spearman’s test, thus, correlations a given in rho. Please note the indications of measurement methods:
- L: LUMINEX
- E: ELISA
- a: activity assay
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools))
install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")Skipping install of 'ggcorrplot' from a github remote, the SHA1 (c46b4cce) has not changed since last install.
Use `force = TRUE` to force installationlibrary(ggcorrplot)
# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA,
select = c(proteins_of_interest_rank_mcp1)
)
# str(AEDB.CEA.temp)
AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)
corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK,
use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
method = "spearman"), 3)
# corr_biomarkers.rank
rename_proteins_of_interest_mcp1 <- c("MCP1 (L, exp2)",
"IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12",
"IL13 (L)", "IL21 (L)",
"INFG", "TNFA", "MIF (L)",
"MCP1 (L, exp1)", "MIP1a (L)", "RANTES (L)", "MIG (L)", "IP10 (L)",
"Eotaxin1 (L)", "TARC (L)", "PARC (L)", "MDC (L)",
"OPG (L)", "sICAM1 (L)", "VEGFA (E)", "TGFB (E)", "MMP2 (a)", "MMP8 (a)", "MMP9 (a)")
colnames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_mcp1)
rownames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_mcp1)
corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")
# ++++++++++++++++++++++++++++
# flattenCorrMatrix
# ++++++++++++++++++++++++++++
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
ut <- upper.tri(cormat)
data.frame(
row = rownames(cormat)[row(cormat)[ut]],
column = rownames(cormat)[col(cormat)[ut]],
cor =(cormat)[ut],
p = pmat[ut]
)
}
corr_biomarkers.rank.df <- flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank)
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "row"] <- "Cytokine_X"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "column"] <- "CytokineY"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "cor"] <- "SpearmanRho"
DT::datatable(corr_biomarkers.rank.df)
fwrite(corr_biomarkers.rank.df, file = paste0(OUT_loc, "/",Today,".correlation_cytokines.txt"))
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
p1 <- ggcorrplot(corr_biomarkers.rank,
method = "square",
type = "lower",
title = "Cross biomarker correlations",
show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
show.diag = TRUE,
hc.order = FALSE,
lab = FALSE,
digits = 3,
tl.cex = 6,
# xlab = c("MCP1"),
# p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
colors = c("#1290D9", "#FFFFFF", "#E55738"))
p1
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.png"), plot = last_plot())Saving 7.29 x 4.51 in image
rm(p1)
While visually actractive we are not necessarily interested in the correlations between all the cytokines, rather of MCP1 with other cytokines only.
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "MCP1 (L, exp2)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/29)
p_threshold[1] 2.763428p1 <- ggbarplot(temp, x = "CytokineY", y = "SpearmanRho",
fill = "CytokineY", # change fill color by cyl
# color = "white", # Set bar border colors to white
palette = uithof_color, # jco journal color palett. see ?ggpar
xlab = "Cytokine",
ylab = expression("Spearman's"~italic(rho)),
sort.val = "desc", # Sort the value in dscending order
sort.by.groups = FALSE, # Don't sort inside each group
x.text.angle = 45, # Rotate vertically x axis texts
cex = 0.8
)
ggpar(p1, legend = "bottom",
legend.title = "") +
theme(axis.text.x = element_text(size = 9),
axis.text.y = element_text(size = 9)) 
rm(p1)Another version - problably not good.
p1 <- ggdotchart(temp, x = "CytokineY", y = "p_log10",
color = "CytokineY", # Color by groups
palette = uithof_color, # Custom color palette
xlab = "Cytokine",
ylab = expression(log[10]~"("~italic(p)~")-value"),
ylim = c(0, 6),
sorting = "descending", # Sort value in descending order
add = "segments", # Add segments from y = 0 to dots
rotate = FALSE, # Rotate vertically
# group = "CytokineY", # Order by groups
dot.size = 8, # Large dot size
label = round(temp$SpearmanRho, digits = 3), # Add mpg values as dot labels
font.label = list(color = "white", size = 8,
vjust = 0.5)
)
ggpar(p1, legend = "bottom",
legend.title = "") +
theme(axis.text.x = element_text(size = 9),
axis.text.y = element_text(size = 9))
# rm(temp, p1)
MCP1 vs. cytokines plaque levels lm()
Model 1
In this model we correct for Age and Gender.
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Analysis of plaque cytokines traits as a function of serum/plaque MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(proteins_of_interest_rank)) {
TRAIT = proteins_of_interest_rank[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender, data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of MCP1_pg_ug_2015_rank.
- processing IL2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
-0.1871
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0138 -0.7263 -0.1541 0.4375 3.1658
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.126985 0.448729 -0.283 0.777
currentDF[, TRAIT] 0.068417 0.057955 1.181 0.239
Age -0.001586 0.006519 -0.243 0.808
Gendermale 0.069623 0.123140 0.565 0.572
Residual standard error: 1.045 on 337 degrees of freedom
Multiple R-squared: 0.005043, Adjusted R-squared: -0.003814
F-statistic: 0.5694 on 3 and 337 DF, p-value: 0.6355
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL2_rank
Effect size...............: 0.068417
Standard error............: 0.057955
Odds ratio (effect size)..: 1.071
Lower 95% CI..............: 0.956
Upper 95% CI..............: 1.2
T-value...................: 1.180503
P-value...................: 0.2386327
R^2.......................: 0.005043
Adjusted r^2..............: -0.003814
Sample size of AE DB......: 2421
Sample size of model......: 341
Missing data %............: 85.91491
- processing IL4_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
-0.1461
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1833 -0.7351 -0.1788 0.4975 3.2187
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.273395 0.461377 -0.593 0.554
currentDF[, TRAIT] 0.022799 0.060030 0.380 0.704
Age 0.001119 0.006711 0.167 0.868
Gendermale 0.074316 0.129082 0.576 0.565
Residual standard error: 1.033 on 312 degrees of freedom
Multiple R-squared: 0.001415, Adjusted R-squared: -0.008186
F-statistic: 0.1474 on 3 and 312 DF, p-value: 0.9313
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL4_rank
Effect size...............: 0.022799
Standard error............: 0.06003
Odds ratio (effect size)..: 1.023
Lower 95% CI..............: 0.91
Upper 95% CI..............: 1.151
T-value...................: 0.379795
P-value...................: 0.7043557
R^2.......................: 0.001415
Adjusted r^2..............: -0.008186
Sample size of AE DB......: 2421
Sample size of model......: 316
Missing data %............: 86.94754
- processing IL5_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
-0.1481
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1489 -0.7125 -0.1506 0.4735 3.2342
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.1984677 0.4340793 -0.457 0.648
currentDF[, TRAIT] 0.0457356 0.0561918 0.814 0.416
Age -0.0003759 0.0063395 -0.059 0.953
Gendermale 0.1114000 0.1206326 0.923 0.356
Residual standard error: 1.017 on 334 degrees of freedom
Multiple R-squared: 0.00427, Adjusted R-squared: -0.004674
F-statistic: 0.4774 on 3 and 334 DF, p-value: 0.6982
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL5_rank
Effect size...............: 0.045736
Standard error............: 0.056192
Odds ratio (effect size)..: 1.047
Lower 95% CI..............: 0.938
Upper 95% CI..............: 1.169
T-value...................: 0.813918
P-value...................: 0.4162716
R^2.......................: 0.00427
Adjusted r^2..............: -0.004674
Sample size of AE DB......: 2421
Sample size of model......: 338
Missing data %............: 86.03883
- processing IL6_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
-0.2321
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0907 -0.7289 -0.1173 0.4877 3.3012
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.097637 0.467842 -0.209 0.835
currentDF[, TRAIT] 0.047978 0.058966 0.814 0.416
Age -0.003265 0.006785 -0.481 0.631
Gendermale 0.122472 0.127312 0.962 0.337
Residual standard error: 1.095 on 348 degrees of freedom
Multiple R-squared: 0.004861, Adjusted R-squared: -0.003717
F-statistic: 0.5667 on 3 and 348 DF, p-value: 0.6373
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL6_rank
Effect size...............: 0.047978
Standard error............: 0.058966
Odds ratio (effect size)..: 1.049
Lower 95% CI..............: 0.935
Upper 95% CI..............: 1.178
T-value...................: 0.813645
P-value...................: 0.4164044
R^2.......................: 0.004861
Adjusted r^2..............: -0.003717
Sample size of AE DB......: 2421
Sample size of model......: 352
Missing data %............: 85.46055
- processing IL8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age
0.40941 0.11232 -0.01017
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2363 -0.7169 -0.0887 0.4757 3.3067
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.301998 0.475678 0.635 0.5259
currentDF[, TRAIT] 0.103929 0.059601 1.744 0.0821 .
Age -0.009993 0.006810 -1.467 0.1432
Gendermale 0.136446 0.129655 1.052 0.2934
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.09 on 338 degrees of freedom
Multiple R-squared: 0.01861, Adjusted R-squared: 0.009896
F-statistic: 2.136 on 3 and 338 DF, p-value: 0.09545
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL8_rank
Effect size...............: 0.103929
Standard error............: 0.059601
Odds ratio (effect size)..: 1.11
Lower 95% CI..............: 0.987
Upper 95% CI..............: 1.247
T-value...................: 1.743739
P-value...................: 0.08211378
R^2.......................: 0.018606
Adjusted r^2..............: 0.009896
Sample size of AE DB......: 2421
Sample size of model......: 342
Missing data %............: 85.87361
- processing IL9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
-0.3089 0.1200
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1515 -0.7544 -0.1109 0.5568 3.3009
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.169209 0.459466 0.368 0.7129
currentDF[, TRAIT] 0.110359 0.058924 1.873 0.0619 .
Age -0.008347 0.006647 -1.256 0.2100
Gendermale 0.125953 0.123899 1.017 0.3100
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.118 on 377 degrees of freedom
Multiple R-squared: 0.01761, Adjusted R-squared: 0.00979
F-statistic: 2.252 on 3 and 377 DF, p-value: 0.08189
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL9_rank
Effect size...............: 0.110359
Standard error............: 0.058924
Odds ratio (effect size)..: 1.117
Lower 95% CI..............: 0.995
Upper 95% CI..............: 1.253
T-value...................: 1.872907
P-value...................: 0.06185486
R^2.......................: 0.017607
Adjusted r^2..............: 0.00979
Sample size of AE DB......: 2421
Sample size of model......: 381
Missing data %............: 84.2627
- processing IL10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
-0.1469
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0945 -0.7232 -0.1486 0.4485 3.2098
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.2746364 0.4684805 -0.586 0.558
currentDF[, TRAIT] 0.0849810 0.0628692 1.352 0.177
Age 0.0005042 0.0068648 0.073 0.942
Gendermale 0.1389622 0.1319915 1.053 0.293
Residual standard error: 1.04 on 297 degrees of freedom
Multiple R-squared: 0.008715, Adjusted R-squared: -0.001298
F-statistic: 0.8704 on 3 and 297 DF, p-value: 0.4568
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL10_rank
Effect size...............: 0.084981
Standard error............: 0.062869
Odds ratio (effect size)..: 1.089
Lower 95% CI..............: 0.962
Upper 95% CI..............: 1.231
T-value...................: 1.351712
P-value...................: 0.1774961
R^2.......................: 0.008715
Adjusted r^2..............: -0.001298
Sample size of AE DB......: 2421
Sample size of model......: 301
Missing data %............: 87.56712
- processing IL12_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
-0.1817
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0695 -0.7182 -0.1613 0.4479 3.2230
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.395537 0.458264 -0.863 0.389
currentDF[, TRAIT] 0.077047 0.059795 1.289 0.199
Age 0.002087 0.006733 0.310 0.757
Gendermale 0.106411 0.127040 0.838 0.403
Residual standard error: 1.032 on 312 degrees of freedom
Multiple R-squared: 0.00732, Adjusted R-squared: -0.002225
F-statistic: 0.7669 on 3 and 312 DF, p-value: 0.5133
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL12_rank
Effect size...............: 0.077047
Standard error............: 0.059795
Odds ratio (effect size)..: 1.08
Lower 95% CI..............: 0.961
Upper 95% CI..............: 1.214
T-value...................: 1.288522
P-value...................: 0.198519
R^2.......................: 0.00732
Adjusted r^2..............: -0.002225
Sample size of AE DB......: 2421
Sample size of model......: 316
Missing data %............: 86.94754
- processing IL13_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
-0.3001 0.1061
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1459 -0.7317 -0.1060 0.5223 3.2560
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.118249 0.413981 0.286 0.7753
currentDF[, TRAIT] 0.095865 0.053668 1.786 0.0748 .
Age -0.007198 0.006004 -1.199 0.2312
Gendermale 0.099873 0.112423 0.888 0.3748
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.082 on 430 degrees of freedom
Multiple R-squared: 0.01406, Adjusted R-squared: 0.007179
F-statistic: 2.044 on 3 and 430 DF, p-value: 0.1071
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL13_rank
Effect size...............: 0.095865
Standard error............: 0.053668
Odds ratio (effect size)..: 1.101
Lower 95% CI..............: 0.991
Upper 95% CI..............: 1.223
T-value...................: 1.786256
P-value...................: 0.0747623
R^2.......................: 0.014058
Adjusted r^2..............: 0.007179
Sample size of AE DB......: 2421
Sample size of model......: 434
Missing data %............: 82.07352
- processing IL21_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
-0.2990 0.1013
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1326 -0.7218 -0.0901 0.5073 3.2716
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.126296 0.412433 0.306 0.7596
currentDF[, TRAIT] 0.090108 0.054130 1.665 0.0967 .
Age -0.007244 0.005975 -1.212 0.2260
Gendermale 0.094794 0.112227 0.845 0.3988
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.081 on 431 degrees of freedom
Multiple R-squared: 0.01303, Adjusted R-squared: 0.006158
F-statistic: 1.896 on 3 and 431 DF, p-value: 0.1295
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL21_rank
Effect size...............: 0.090108
Standard error............: 0.05413
Odds ratio (effect size)..: 1.094
Lower 95% CI..............: 0.984
Upper 95% CI..............: 1.217
T-value...................: 1.664666
P-value...................: 0.09670626
R^2.......................: 0.013028
Adjusted r^2..............: 0.006158
Sample size of AE DB......: 2421
Sample size of model......: 435
Missing data %............: 82.03222
- processing INFG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale
-0.3374 0.1641 0.1825
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0061 -0.7195 -0.1358 0.4739 3.1951
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.153946 0.461624 -0.333 0.73898
currentDF[, TRAIT] 0.161772 0.061892 2.614 0.00936 **
Age -0.002728 0.006680 -0.408 0.68327
Gendermale 0.183410 0.127424 1.439 0.15099
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.06 on 332 degrees of freedom
Multiple R-squared: 0.02468, Adjusted R-squared: 0.01586
F-statistic: 2.8 on 3 and 332 DF, p-value: 0.04005
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: INFG_rank
Effect size...............: 0.161772
Standard error............: 0.061892
Odds ratio (effect size)..: 1.176
Lower 95% CI..............: 1.041
Upper 95% CI..............: 1.327
T-value...................: 2.613754
P-value...................: 0.009363151
R^2.......................: 0.024678
Adjusted r^2..............: 0.015864
Sample size of AE DB......: 2421
Sample size of model......: 336
Missing data %............: 86.12144
- processing TNFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
-0.1969
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0969 -0.7002 -0.1285 0.4529 3.2669
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.121674 0.461335 -0.264 0.792
currentDF[, TRAIT] 0.071417 0.060266 1.185 0.237
Age -0.002395 0.006720 -0.356 0.722
Gendermale 0.132059 0.127121 1.039 0.300
Residual standard error: 1.022 on 302 degrees of freedom
Multiple R-squared: 0.007868, Adjusted R-squared: -0.001987
F-statistic: 0.7983 on 3 and 302 DF, p-value: 0.4956
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: TNFA_rank
Effect size...............: 0.071417
Standard error............: 0.060266
Odds ratio (effect size)..: 1.074
Lower 95% CI..............: 0.954
Upper 95% CI..............: 1.209
T-value...................: 1.185028
P-value...................: 0.2369379
R^2.......................: 0.007868
Adjusted r^2..............: -0.001987
Sample size of AE DB......: 2421
Sample size of model......: 306
Missing data %............: 87.3606
- processing MIF_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
-0.3051 0.1823
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9832 -0.7831 -0.1190 0.5508 3.0869
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.040947 0.408856 0.100 0.920271
currentDF[, TRAIT] 0.175121 0.052085 3.362 0.000842 ***
Age -0.006138 0.005913 -1.038 0.299837
Gendermale 0.101370 0.110823 0.915 0.360861
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.071 on 431 degrees of freedom
Multiple R-squared: 0.03207, Adjusted R-squared: 0.02533
F-statistic: 4.76 on 3 and 431 DF, p-value: 0.002818
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MIF_rank
Effect size...............: 0.175121
Standard error............: 0.052085
Odds ratio (effect size)..: 1.191
Lower 95% CI..............: 1.076
Upper 95% CI..............: 1.319
T-value...................: 3.362233
P-value...................: 0.0008421348
R^2.......................: 0.03207
Adjusted r^2..............: 0.025333
Sample size of AE DB......: 2421
Sample size of model......: 435
Missing data %............: 82.03222
- processing MCP1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
-0.3035 0.2754
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3617 -0.7092 -0.1035 0.5611 3.1254
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.073056 0.403041 0.181 0.856
currentDF[, TRAIT] 0.267726 0.050486 5.303 1.83e-07 ***
Age -0.006272 0.005839 -1.074 0.283
Gendermale 0.069924 0.109679 0.638 0.524
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.049 on 427 degrees of freedom
Multiple R-squared: 0.06926, Adjusted R-squared: 0.06272
F-statistic: 10.59 on 3 and 427 DF, p-value: 9.916e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MCP1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MCP1_rank
Effect size...............: 0.267726
Standard error............: 0.050486
Odds ratio (effect size)..: 1.307
Lower 95% CI..............: 1.184
Upper 95% CI..............: 1.443
T-value...................: 5.303001
P-value...................: 1.831903e-07
R^2.......................: 0.069257
Adjusted r^2..............: 0.062718
Sample size of AE DB......: 2421
Sample size of model......: 431
Missing data %............: 82.19744
- processing MIP1a_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age
0.329736 0.117434 -0.009516
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1508 -0.7331 -0.1191 0.5654 3.2827
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.266367 0.445283 0.598 0.5501
currentDF[, TRAIT] 0.113586 0.057165 1.987 0.0476 *
Age -0.009732 0.006457 -1.507 0.1325
Gendermale 0.112525 0.121253 0.928 0.3540
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.107 on 390 degrees of freedom
Multiple R-squared: 0.01986, Adjusted R-squared: 0.01232
F-statistic: 2.634 on 3 and 390 DF, p-value: 0.04956
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MIP1a_rank
Effect size...............: 0.113586
Standard error............: 0.057165
Odds ratio (effect size)..: 1.12
Lower 95% CI..............: 1.002
Upper 95% CI..............: 1.253
T-value...................: 1.98698
P-value...................: 0.04762379
R^2.......................: 0.019863
Adjusted r^2..............: 0.012323
Sample size of AE DB......: 2421
Sample size of model......: 394
Missing data %............: 83.72573
- processing RANTES_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
-0.3011 0.1694
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9552 -0.7469 -0.1216 0.5535 3.1184
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.021100 0.413532 0.051 0.95933
currentDF[, TRAIT] 0.160303 0.052573 3.049 0.00244 **
Age -0.005775 0.006000 -0.962 0.33640
Gendermale 0.098816 0.112897 0.875 0.38192
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.077 on 423 degrees of freedom
Multiple R-squared: 0.02817, Adjusted R-squared: 0.02128
F-statistic: 4.087 on 3 and 423 DF, p-value: 0.007046
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: RANTES_rank
Effect size...............: 0.160303
Standard error............: 0.052573
Odds ratio (effect size)..: 1.174
Lower 95% CI..............: 1.059
Upper 95% CI..............: 1.301
T-value...................: 3.049156
P-value...................: 0.002438996
R^2.......................: 0.028169
Adjusted r^2..............: 0.021277
Sample size of AE DB......: 2421
Sample size of model......: 427
Missing data %............: 82.36266
- processing MIG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age, data = currentDF)
Coefficients:
(Intercept) Age
0.345456 -0.009483
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0769 -0.7220 -0.0850 0.5347 3.3334
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.299870 0.423296 0.708 0.479
currentDF[, TRAIT] -0.017754 0.054021 -0.329 0.743
Age -0.009856 0.006131 -1.608 0.109
Gendermale 0.102630 0.114473 0.897 0.370
Residual standard error: 1.091 on 420 degrees of freedom
Multiple R-squared: 0.007796, Adjusted R-squared: 0.0007085
F-statistic: 1.1 on 3 and 420 DF, p-value: 0.3489
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MIG_rank
Effect size...............: -0.017754
Standard error............: 0.054021
Odds ratio (effect size)..: 0.982
Lower 95% CI..............: 0.884
Upper 95% CI..............: 1.092
T-value...................: -0.328644
P-value...................: 0.7425884
R^2.......................: 0.007796
Adjusted r^2..............: 0.000708
Sample size of AE DB......: 2421
Sample size of model......: 424
Missing data %............: 82.48658
- processing IP10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
-0.3207 0.1701
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0811 -0.7490 -0.0769 0.5478 3.2071
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.016843 0.440525 0.038 0.96952
currentDF[, TRAIT] 0.165313 0.055564 2.975 0.00311 **
Age -0.006146 0.006411 -0.959 0.33834
Gendermale 0.114371 0.118226 0.967 0.33396
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.084 on 384 degrees of freedom
Multiple R-squared: 0.02852, Adjusted R-squared: 0.02093
F-statistic: 3.758 on 3 and 384 DF, p-value: 0.01107
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IP10_rank
Effect size...............: 0.165313
Standard error............: 0.055564
Odds ratio (effect size)..: 1.18
Lower 95% CI..............: 1.058
Upper 95% CI..............: 1.316
T-value...................: 2.975202
P-value...................: 0.003113081
R^2.......................: 0.02852
Adjusted r^2..............: 0.02093
Sample size of AE DB......: 2421
Sample size of model......: 388
Missing data %............: 83.97356
- processing Eotaxin1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
-0.2979
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1174 -0.7320 -0.0801 0.5330 3.3284
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.187312 0.412108 0.455 0.650
currentDF[, TRAIT] 0.024976 0.054611 0.457 0.648
Age -0.008223 0.005969 -1.378 0.169
Gendermale 0.103651 0.112922 0.918 0.359
Residual standard error: 1.085 on 431 degrees of freedom
Multiple R-squared: 0.007164, Adjusted R-squared: 0.0002537
F-statistic: 1.037 on 3 and 431 DF, p-value: 0.3761
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 0.024976
Standard error............: 0.054611
Odds ratio (effect size)..: 1.025
Lower 95% CI..............: 0.921
Upper 95% CI..............: 1.141
T-value...................: 0.457347
P-value...................: 0.6476522
R^2.......................: 0.007164
Adjusted r^2..............: 0.000254
Sample size of AE DB......: 2421
Sample size of model......: 435
Missing data %............: 82.03222
- processing TARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
-0.3658 0.2060
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0965 -0.7023 -0.0993 0.5066 3.2856
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.142221 0.427135 0.333 0.739350
currentDF[, TRAIT] 0.196073 0.057454 3.413 0.000715 ***
Age -0.008298 0.006180 -1.343 0.180168
Gendermale 0.078589 0.117638 0.668 0.504512
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.056 on 369 degrees of freedom
Multiple R-squared: 0.03976, Adjusted R-squared: 0.03195
F-statistic: 5.093 on 3 and 369 DF, p-value: 0.001824
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: TARC_rank
Effect size...............: 0.196073
Standard error............: 0.057454
Odds ratio (effect size)..: 1.217
Lower 95% CI..............: 1.087
Upper 95% CI..............: 1.362
T-value...................: 3.412675
P-value...................: 0.0007146118
R^2.......................: 0.03976
Adjusted r^2..............: 0.031953
Sample size of AE DB......: 2421
Sample size of model......: 373
Missing data %............: 84.59314
- processing PARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
-0.3074 0.2099
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0206 -0.7081 -0.1022 0.5607 2.9481
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.087527 0.405118 0.216 0.829
currentDF[, TRAIT] 0.207826 0.052435 3.964 8.64e-05 ***
Age -0.007072 0.005855 -1.208 0.228
Gendermale 0.121974 0.110295 1.106 0.269
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.066 on 431 degrees of freedom
Multiple R-squared: 0.04161, Adjusted R-squared: 0.03494
F-statistic: 6.238 on 3 and 431 DF, p-value: 0.0003741
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: PARC_rank
Effect size...............: 0.207826
Standard error............: 0.052435
Odds ratio (effect size)..: 1.231
Lower 95% CI..............: 1.111
Upper 95% CI..............: 1.364
T-value...................: 3.96351
P-value...................: 8.643532e-05
R^2.......................: 0.041615
Adjusted r^2..............: 0.034944
Sample size of AE DB......: 2421
Sample size of model......: 435
Missing data %............: 82.03222
- processing MDC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age
0.314480 0.158713 -0.009263
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1392 -0.7839 -0.0796 0.5834 3.1870
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.239102 0.442273 0.541 0.58908
currentDF[, TRAIT] 0.160688 0.056564 2.841 0.00474 **
Age -0.009502 0.006410 -1.482 0.13906
Gendermale 0.133043 0.120548 1.104 0.27043
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.103 on 387 degrees of freedom
Multiple R-squared: 0.03083, Adjusted R-squared: 0.02332
F-statistic: 4.103 on 3 and 387 DF, p-value: 0.006937
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MDC_rank
Effect size...............: 0.160688
Standard error............: 0.056564
Odds ratio (effect size)..: 1.174
Lower 95% CI..............: 1.051
Upper 95% CI..............: 1.312
T-value...................: 2.840822
P-value...................: 0.004737585
R^2.......................: 0.030829
Adjusted r^2..............: 0.023316
Sample size of AE DB......: 2421
Sample size of model......: 391
Missing data %............: 83.84965
- processing OPG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
-0.3001 0.1712
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9243 -0.7253 -0.0788 0.5495 3.1685
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.127052 0.408695 0.311 0.75605
currentDF[, TRAIT] 0.163866 0.051508 3.181 0.00157 **
Age -0.007145 0.005926 -1.206 0.22856
Gendermale 0.081692 0.111745 0.731 0.46514
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.074 on 430 degrees of freedom
Multiple R-squared: 0.02958, Adjusted R-squared: 0.02281
F-statistic: 4.369 on 3 and 430 DF, p-value: 0.004796
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: OPG_rank
Effect size...............: 0.163866
Standard error............: 0.051508
Odds ratio (effect size)..: 1.178
Lower 95% CI..............: 1.065
Upper 95% CI..............: 1.303
T-value...................: 3.181357
P-value...................: 0.001572083
R^2.......................: 0.029583
Adjusted r^2..............: 0.022812
Sample size of AE DB......: 2421
Sample size of model......: 434
Missing data %............: 82.07352
- processing sICAM1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
-0.3014 0.1592
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0829 -0.7520 -0.0844 0.5232 3.1590
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.007216 0.412317 0.018 0.98605
currentDF[, TRAIT] 0.152689 0.051253 2.979 0.00305 **
Age -0.005724 0.005960 -0.960 0.33741
Gendermale 0.114929 0.111118 1.034 0.30158
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.074 on 431 degrees of freedom
Multiple R-squared: 0.02672, Adjusted R-squared: 0.01995
F-statistic: 3.945 on 3 and 431 DF, p-value: 0.00853
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: sICAM1_rank
Effect size...............: 0.152689
Standard error............: 0.051253
Odds ratio (effect size)..: 1.165
Lower 95% CI..............: 1.054
Upper 95% CI..............: 1.288
T-value...................: 2.979145
P-value...................: 0.003053889
R^2.......................: 0.026725
Adjusted r^2..............: 0.01995
Sample size of AE DB......: 2421
Sample size of model......: 435
Missing data %............: 82.03222
- processing VEGFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)
Coefficients:
(Intercept)
-0.349
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0509 -0.7061 -0.0870 0.5261 3.4293
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.068883 0.439439 0.157 0.876
currentDF[, TRAIT] 0.004020 0.055126 0.073 0.942
Age -0.007853 0.006297 -1.247 0.213
Gendermale 0.162015 0.121468 1.334 0.183
Residual standard error: 1.06 on 364 degrees of freedom
Multiple R-squared: 0.009134, Adjusted R-squared: 0.0009677
F-statistic: 1.118 on 3 and 364 DF, p-value: 0.3415
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: VEGFA_rank
Effect size...............: 0.00402
Standard error............: 0.055126
Odds ratio (effect size)..: 1.004
Lower 95% CI..............: 0.901
Upper 95% CI..............: 1.119
T-value...................: 0.072928
P-value...................: 0.9419038
R^2.......................: 0.009134
Adjusted r^2..............: 0.000968
Sample size of AE DB......: 2421
Sample size of model......: 368
Missing data %............: 84.79967
- processing TGFB_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
-0.2787 0.1345
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9736 -0.7129 -0.1016 0.4965 3.1937
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.157291 0.416652 0.378 0.706
currentDF[, TRAIT] 0.132473 0.053085 2.495 0.013 *
Age -0.007760 0.006024 -1.288 0.198
Gendermale 0.127319 0.114857 1.108 0.268
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.09 on 421 degrees of freedom
Multiple R-squared: 0.02161, Adjusted R-squared: 0.01464
F-statistic: 3.099 on 3 and 421 DF, p-value: 0.02666
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: TGFB_rank
Effect size...............: 0.132473
Standard error............: 0.053085
Odds ratio (effect size)..: 1.142
Lower 95% CI..............: 1.029
Upper 95% CI..............: 1.267
T-value...................: 2.495473
P-value...................: 0.01296082
R^2.......................: 0.021608
Adjusted r^2..............: 0.014636
Sample size of AE DB......: 2421
Sample size of model......: 425
Missing data %............: 82.44527
- processing MMP2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale
-0.4434 0.1667 0.1959
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2756 -0.7509 -0.0728 0.5125 3.2664
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.022016 0.417955 0.053 0.95801
currentDF[, TRAIT] 0.160516 0.054876 2.925 0.00363 **
Age -0.006893 0.006028 -1.143 0.25353
Gendermale 0.195535 0.114344 1.710 0.08798 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.091 on 426 degrees of freedom
Multiple R-squared: 0.02847, Adjusted R-squared: 0.02163
F-statistic: 4.162 on 3 and 426 DF, p-value: 0.006362
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MMP2_rank
Effect size...............: 0.160516
Standard error............: 0.054876
Odds ratio (effect size)..: 1.174
Lower 95% CI..............: 1.054
Upper 95% CI..............: 1.307
T-value...................: 2.925092
P-value...................: 0.003627529
R^2.......................: 0.028474
Adjusted r^2..............: 0.021633
Sample size of AE DB......: 2421
Sample size of model......: 430
Missing data %............: 82.23874
- processing MMP8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
-0.3044 0.1515
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2735 -0.7497 -0.0894 0.5297 3.1583
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.197589 0.415893 0.475 0.6350
currentDF[, TRAIT] 0.142794 0.054886 2.602 0.0096 **
Age -0.008529 0.006012 -1.419 0.1567
Gendermale 0.106678 0.115401 0.924 0.3558
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.093 on 426 degrees of freedom
Multiple R-squared: 0.02446, Adjusted R-squared: 0.01759
F-statistic: 3.561 on 3 and 426 DF, p-value: 0.01434
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MMP8_rank
Effect size...............: 0.142794
Standard error............: 0.054886
Odds ratio (effect size)..: 1.153
Lower 95% CI..............: 1.036
Upper 95% CI..............: 1.285
T-value...................: 2.601622
P-value...................: 0.009601555
R^2.......................: 0.024461
Adjusted r^2..............: 0.017591
Sample size of AE DB......: 2421
Sample size of model......: 430
Missing data %............: 82.23874
- processing MMP9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age
0.289977 0.145540 -0.008894
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2652 -0.7691 -0.0965 0.5318 3.2400
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.203538 0.415830 0.489 0.62476
currentDF[, TRAIT] 0.140326 0.052965 2.649 0.00836 **
Age -0.008940 0.006011 -1.487 0.13767
Gendermale 0.130474 0.114190 1.143 0.25385
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.093 on 426 degrees of freedom
Multiple R-squared: 0.02503, Adjusted R-squared: 0.01816
F-statistic: 3.645 on 3 and 426 DF, p-value: 0.0128
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MMP9_rank
Effect size...............: 0.140326
Standard error............: 0.052965
Odds ratio (effect size)..: 1.151
Lower 95% CI..............: 1.037
Upper 95% CI..............: 1.277
T-value...................: 2.649437
P-value...................: 0.008362477
R^2.......................: 0.025027
Adjusted r^2..............: 0.018161
Sample size of AE DB......: 2421
Sample size of model......: 430
Missing data %............: 82.23874 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)DT::datatable(GLM.results)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.MCP1_Plaque.Cytokines_Plaques.RANK.MODEL1.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Model 2
In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis.
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Analysis of plaque cytokines as a function of serum/plaque MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(proteins_of_interest_rank)) {
TRAIT = proteins_of_interest_rank[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender +
Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose,
data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of MCP1_pg_ug_2015_rank.
- processing IL2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ SmokerStatus + Med.Statin.LLD +
MedHx_CVD + stenose, data = currentDF)
Coefficients:
(Intercept) SmokerStatusEx-smoker SmokerStatusNever smoked Med.Statin.LLDyes MedHx_CVDyes stenose50-70% stenose70-90%
-1.4116 -0.1971 0.2067 -0.3136 0.1780 0.8013 1.5344
stenose90-99% stenose100% (Occlusion)
1.4295 0.8031
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8738 -0.6736 -0.1345 0.4803 3.1937
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.9040372 1.3804366 -0.655 0.5130
currentDF[, TRAIT] 0.0609237 0.0611933 0.996 0.3203
Age -0.0008281 0.0076152 -0.109 0.9135
Gendermale 0.1152108 0.1322087 0.871 0.3842
Hypertension.compositeyes -0.1944219 0.1815830 -1.071 0.2852
DiabetesStatusDiabetes 0.0484443 0.1521821 0.318 0.7505
SmokerStatusEx-smoker -0.1935160 0.1347748 -1.436 0.1521
SmokerStatusNever smoked 0.2799564 0.2107724 1.328 0.1851
Med.Statin.LLDyes -0.2813017 0.1345181 -2.091 0.0374 *
Med.all.antiplateletyes -0.1563247 0.2226827 -0.702 0.4832
GFR_MDRD 0.0003522 0.0034326 0.103 0.9183
BMI -0.0059270 0.0181250 -0.327 0.7439
MedHx_CVDyes 0.1882475 0.1253976 1.501 0.1344
stenose50-70% 0.5857424 1.0938979 0.535 0.5927
stenose70-90% 1.3935845 1.0421384 1.337 0.1822
stenose90-99% 1.3042041 1.0403354 1.254 0.2110
stenose100% (Occlusion) 0.4560656 1.2264521 0.372 0.7103
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.027 on 296 degrees of freedom
Multiple R-squared: 0.07597, Adjusted R-squared: 0.02602
F-statistic: 1.521 on 16 and 296 DF, p-value: 0.09096
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL2_rank
Effect size...............: 0.060924
Standard error............: 0.061193
Odds ratio (effect size)..: 1.063
Lower 95% CI..............: 0.943
Upper 95% CI..............: 1.198
T-value...................: 0.995594
P-value...................: 0.3202605
R^2.......................: 0.075969
Adjusted r^2..............: 0.026022
Sample size of AE DB......: 2421
Sample size of model......: 313
Missing data %............: 87.07146
- processing IL4_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ SmokerStatus + Med.Statin.LLD +
MedHx_CVD + stenose, data = currentDF)
Coefficients:
(Intercept) SmokerStatusEx-smoker SmokerStatusNever smoked Med.Statin.LLDyes MedHx_CVDyes stenose70-90% stenose90-99%
-0.59636 -0.21519 0.32292 -0.35784 0.19828 0.77301 0.68296
stenose100% (Occlusion)
-0.03684
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0207 -0.6799 -0.1317 0.5013 3.1727
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.2148910 0.9394517 -0.229 0.8192
currentDF[, TRAIT] 0.0131585 0.0636832 0.207 0.8365
Age 0.0001100 0.0078523 0.014 0.9888
Gendermale 0.1170079 0.1382040 0.847 0.3979
Hypertension.compositeyes -0.2447199 0.1892615 -1.293 0.1971
DiabetesStatusDiabetes 0.0277828 0.1553730 0.179 0.8582
SmokerStatusEx-smoker -0.1987040 0.1361226 -1.460 0.1455
SmokerStatusNever smoked 0.4070975 0.2229495 1.826 0.0690 .
Med.Statin.LLDyes -0.3334599 0.1394504 -2.391 0.0175 *
Med.all.antiplateletyes -0.1590064 0.2334238 -0.681 0.4963
GFR_MDRD 0.0007895 0.0037210 0.212 0.8321
BMI -0.0109164 0.0185297 -0.589 0.5563
MedHx_CVDyes 0.2036860 0.1294762 1.573 0.1168
stenose70-90% 0.8402572 0.3465787 2.424 0.0160 *
stenose90-99% 0.7757418 0.3408006 2.276 0.0236 *
stenose100% (Occlusion) -0.2042458 0.7156606 -0.285 0.7756
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.014 on 272 degrees of freedom
Multiple R-squared: 0.08574, Adjusted R-squared: 0.03532
F-statistic: 1.701 on 15 and 272 DF, p-value: 0.05052
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL4_rank
Effect size...............: 0.013159
Standard error............: 0.063683
Odds ratio (effect size)..: 1.013
Lower 95% CI..............: 0.894
Upper 95% CI..............: 1.148
T-value...................: 0.206624
P-value...................: 0.8364577
R^2.......................: 0.08574
Adjusted r^2..............: 0.035321
Sample size of AE DB......: 2421
Sample size of model......: 288
Missing data %............: 88.10409
- processing IL5_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ SmokerStatus + Med.Statin.LLD +
MedHx_CVD, data = currentDF)
Coefficients:
(Intercept) SmokerStatusEx-smoker SmokerStatusNever smoked Med.Statin.LLDyes MedHx_CVDyes
0.04725 -0.18936 0.25675 -0.33530 0.21401
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0486 -0.6399 -0.1424 0.4628 3.1633
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.134e-01 8.693e-01 -0.130 0.8963
currentDF[, TRAIT] 2.441e-02 5.874e-02 0.416 0.6780
Age -6.869e-04 7.485e-03 -0.092 0.9269
Gendermale 1.257e-01 1.293e-01 0.972 0.3318
Hypertension.compositeyes -2.627e-01 1.759e-01 -1.493 0.1365
DiabetesStatusDiabetes -6.447e-02 1.501e-01 -0.429 0.6679
SmokerStatusEx-smoker -1.839e-01 1.301e-01 -1.414 0.1585
SmokerStatusNever smoked 3.254e-01 2.152e-01 1.512 0.1315
Med.Statin.LLDyes -3.144e-01 1.313e-01 -2.395 0.0173 *
Med.all.antiplateletyes -1.660e-01 2.239e-01 -0.742 0.4590
GFR_MDRD 5.308e-05 3.421e-03 0.016 0.9876
BMI -4.430e-04 1.725e-02 -0.026 0.9795
MedHx_CVDyes 2.150e-01 1.221e-01 1.761 0.0792 .
stenose70-90% 5.506e-01 3.364e-01 1.637 0.1028
stenose90-99% 5.046e-01 3.333e-01 1.514 0.1312
stenose100% (Occlusion) -4.019e-01 7.018e-01 -0.573 0.5673
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9959 on 291 degrees of freedom
Multiple R-squared: 0.07753, Adjusted R-squared: 0.02998
F-statistic: 1.631 on 15 and 291 DF, p-value: 0.06503
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL5_rank
Effect size...............: 0.024409
Standard error............: 0.058738
Odds ratio (effect size)..: 1.025
Lower 95% CI..............: 0.913
Upper 95% CI..............: 1.15
T-value...................: 0.415556
P-value...................: 0.6780413
R^2.......................: 0.077534
Adjusted r^2..............: 0.029984
Sample size of AE DB......: 2421
Sample size of model......: 307
Missing data %............: 87.31929
- processing IL6_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ SmokerStatus + Med.Statin.LLD +
MedHx_CVD, data = currentDF)
Coefficients:
(Intercept) SmokerStatusEx-smoker SmokerStatusNever smoked Med.Statin.LLDyes MedHx_CVDyes
0.02765 -0.19701 0.17715 -0.34026 0.18317
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9340 -0.7269 -0.1402 0.4952 3.2485
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.7304495 1.2039040 -0.607 0.5445
currentDF[, TRAIT] 0.0495860 0.0605667 0.819 0.4136
Age 0.0029375 0.0081022 0.363 0.7172
Gendermale 0.0972441 0.1360836 0.715 0.4754
Hypertension.compositeyes -0.1933556 0.1889107 -1.024 0.3069
DiabetesStatusDiabetes 0.0314512 0.1578936 0.199 0.8422
SmokerStatusEx-smoker -0.2041176 0.1380075 -1.479 0.1402
SmokerStatusNever smoked 0.2192621 0.2208572 0.993 0.3216
Med.Statin.LLDyes -0.3213688 0.1375459 -2.336 0.0201 *
Med.all.antiplateletyes -0.0103473 0.2179580 -0.047 0.9622
GFR_MDRD 0.0002681 0.0036520 0.073 0.9415
BMI -0.0029085 0.0173356 -0.168 0.8669
MedHx_CVDyes 0.1939384 0.1280896 1.514 0.1311
stenose50-70% 0.0441398 0.8270848 0.053 0.9575
stenose70-90% 0.8164480 0.7683489 1.063 0.2888
stenose90-99% 0.6929481 0.7643636 0.907 0.3654
stenose100% (Occlusion) 0.0266923 1.0187181 0.026 0.9791
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.063 on 299 degrees of freedom
Multiple R-squared: 0.06977, Adjusted R-squared: 0.01999
F-statistic: 1.402 on 16 and 299 DF, p-value: 0.1392
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL6_rank
Effect size...............: 0.049586
Standard error............: 0.060567
Odds ratio (effect size)..: 1.051
Lower 95% CI..............: 0.933
Upper 95% CI..............: 1.183
T-value...................: 0.8187
P-value...................: 0.4136097
R^2.......................: 0.069768
Adjusted r^2..............: 0.01999
Sample size of AE DB......: 2421
Sample size of model......: 316
Missing data %............: 86.94754
- processing IL8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Med.Statin.LLD + GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Med.Statin.LLDyes GFR_MDRD
1.319866 0.150243 -0.012446 -0.331128 -0.006835
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1660 -0.6878 -0.0854 0.4392 2.7519
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.576633 1.204565 0.479 0.6325
currentDF[, TRAIT] 0.122587 0.062436 1.963 0.0506 .
Age -0.011736 0.008068 -1.455 0.1468
Gendermale 0.156818 0.138854 1.129 0.2597
Hypertension.compositeyes -0.147644 0.191180 -0.772 0.4406
DiabetesStatusDiabetes 0.071065 0.159455 0.446 0.6562
SmokerStatusEx-smoker -0.089324 0.137683 -0.649 0.5170
SmokerStatusNever smoked 0.219589 0.229128 0.958 0.3387
Med.Statin.LLDyes -0.344245 0.136722 -2.518 0.0123 *
Med.all.antiplateletyes 0.109546 0.220699 0.496 0.6200
GFR_MDRD -0.005958 0.003488 -1.708 0.0887 .
BMI -0.006302 0.017262 -0.365 0.7153
MedHx_CVDyes 0.136688 0.129745 1.054 0.2930
stenose50-70% -0.113009 0.831479 -0.136 0.8920
stenose70-90% 0.721268 0.764489 0.943 0.3462
stenose90-99% 0.663030 0.760440 0.872 0.3840
stenose100% (Occlusion) 0.153064 0.994391 0.154 0.8778
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.059 on 290 degrees of freedom
Multiple R-squared: 0.08298, Adjusted R-squared: 0.03239
F-statistic: 1.64 on 16 and 290 DF, p-value: 0.05807
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL8_rank
Effect size...............: 0.122587
Standard error............: 0.062436
Odds ratio (effect size)..: 1.13
Lower 95% CI..............: 1
Upper 95% CI..............: 1.278
T-value...................: 1.963394
P-value...................: 0.05055575
R^2.......................: 0.082985
Adjusted r^2..............: 0.032391
Sample size of AE DB......: 2421
Sample size of model......: 307
Missing data %............: 87.31929
- processing IL9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Med.Statin.LLD + GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Med.Statin.LLDyes GFR_MDRD
1.256361 0.120196 -0.012202 -0.369097 -0.006247
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9999 -0.7439 -0.1274 0.5031 3.3027
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.309953 1.161403 1.128 0.26018
currentDF[, TRAIT] 0.114667 0.061271 1.871 0.06217 .
Age -0.012838 0.007711 -1.665 0.09688 .
Gendermale 0.120691 0.133653 0.903 0.36718
Hypertension.compositeyes -0.166469 0.184521 -0.902 0.36763
DiabetesStatusDiabetes 0.189497 0.157530 1.203 0.22987
SmokerStatusEx-smoker -0.050438 0.136801 -0.369 0.71259
SmokerStatusNever smoked 0.169135 0.202769 0.834 0.40482
Med.Statin.LLDyes -0.390577 0.139332 -2.803 0.00536 **
Med.all.antiplateletyes -0.099250 0.224735 -0.442 0.65905
GFR_MDRD -0.005385 0.003344 -1.610 0.10831
BMI -0.016522 0.016479 -1.003 0.31680
MedHx_CVDyes 0.142001 0.127331 1.115 0.26558
stenose50-70% -0.052841 0.853975 -0.062 0.95070
stenose70-90% 0.458094 0.795729 0.576 0.56522
stenose90-99% 0.420588 0.791481 0.531 0.59550
stenose100% (Occlusion) -0.302480 0.983396 -0.308 0.75859
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.103 on 329 degrees of freedom
Multiple R-squared: 0.07311, Adjusted R-squared: 0.02803
F-statistic: 1.622 on 16 and 329 DF, p-value: 0.06145
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL9_rank
Effect size...............: 0.114667
Standard error............: 0.061271
Odds ratio (effect size)..: 1.121
Lower 95% CI..............: 0.995
Upper 95% CI..............: 1.265
T-value...................: 1.871456
P-value...................: 0.0621692
R^2.......................: 0.073105
Adjusted r^2..............: 0.028029
Sample size of AE DB......: 2421
Sample size of model......: 346
Missing data %............: 85.70839
- processing IL10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ SmokerStatus + Med.Statin.LLD,
data = currentDF)
Coefficients:
(Intercept) SmokerStatusEx-smoker SmokerStatusNever smoked Med.Statin.LLDyes
0.2062 -0.2315 0.2807 -0.3448
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9449 -0.6591 -0.1329 0.5218 3.1719
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.260e-01 9.598e-01 -0.131 0.8956
currentDF[, TRAIT] 6.375e-02 6.650e-02 0.959 0.3387
Age 1.083e-05 8.144e-03 0.001 0.9989
Gendermale 2.001e-01 1.420e-01 1.410 0.1598
Hypertension.compositeyes -2.658e-01 1.971e-01 -1.349 0.1787
DiabetesStatusDiabetes -2.252e-03 1.627e-01 -0.014 0.9890
SmokerStatusEx-smoker -2.216e-01 1.410e-01 -1.572 0.1172
SmokerStatusNever smoked 3.889e-01 2.292e-01 1.697 0.0909 .
Med.Statin.LLDyes -3.198e-01 1.425e-01 -2.244 0.0257 *
Med.all.antiplateletyes -1.847e-01 2.418e-01 -0.764 0.4456
GFR_MDRD 1.158e-05 3.831e-03 0.003 0.9976
BMI -5.242e-03 1.863e-02 -0.281 0.7786
MedHx_CVDyes 1.426e-01 1.364e-01 1.046 0.2967
stenose70-90% 6.467e-01 3.686e-01 1.755 0.0805 .
stenose90-99% 6.346e-01 3.634e-01 1.746 0.0820 .
stenose100% (Occlusion) -3.388e-01 7.351e-01 -0.461 0.6453
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.024 on 259 degrees of freedom
Multiple R-squared: 0.08837, Adjusted R-squared: 0.03558
F-statistic: 1.674 on 15 and 259 DF, p-value: 0.05621
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL10_rank
Effect size...............: 0.063746
Standard error............: 0.066499
Odds ratio (effect size)..: 1.066
Lower 95% CI..............: 0.936
Upper 95% CI..............: 1.214
T-value...................: 0.958602
P-value...................: 0.3386533
R^2.......................: 0.088373
Adjusted r^2..............: 0.035576
Sample size of AE DB......: 2421
Sample size of model......: 275
Missing data %............: 88.64106
- processing IL12_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Hypertension.composite +
SmokerStatus + Med.Statin.LLD + MedHx_CVD + stenose, data = currentDF)
Coefficients:
(Intercept) Hypertension.compositeyes SmokerStatusEx-smoker SmokerStatusNever smoked Med.Statin.LLDyes MedHx_CVDyes stenose70-90%
-0.4426 -0.3246 -0.1677 0.3369 -0.2917 0.1949 0.7764
stenose90-99% stenose100% (Occlusion)
0.7078 0.1502
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9065 -0.6774 -0.1486 0.5077 3.2004
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.421304 0.949003 -0.444 0.6574
currentDF[, TRAIT] 0.072580 0.064218 1.130 0.2594
Age 0.002493 0.008061 0.309 0.7573
Gendermale 0.139809 0.137259 1.019 0.3093
Hypertension.compositeyes -0.355594 0.192595 -1.846 0.0659 .
DiabetesStatusDiabetes 0.036125 0.157758 0.229 0.8190
SmokerStatusEx-smoker -0.198593 0.140640 -1.412 0.1591
SmokerStatusNever smoked 0.341714 0.216600 1.578 0.1158
Med.Statin.LLDyes -0.259660 0.140067 -1.854 0.0648 .
Med.all.antiplateletyes -0.064441 0.234396 -0.275 0.7836
GFR_MDRD -0.000507 0.003728 -0.136 0.8919
BMI -0.007681 0.018628 -0.412 0.6804
MedHx_CVDyes 0.190034 0.131533 1.445 0.1497
stenose70-90% 0.803795 0.347726 2.312 0.0216 *
stenose90-99% 0.737758 0.342741 2.153 0.0322 *
stenose100% (Occlusion) 0.139376 0.840365 0.166 0.8684
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.017 on 271 degrees of freedom
Multiple R-squared: 0.08055, Adjusted R-squared: 0.02966
F-statistic: 1.583 on 15 and 271 DF, p-value: 0.07802
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL12_rank
Effect size...............: 0.07258
Standard error............: 0.064218
Odds ratio (effect size)..: 1.075
Lower 95% CI..............: 0.948
Upper 95% CI..............: 1.22
T-value...................: 1.130204
P-value...................: 0.2593895
R^2.......................: 0.08055
Adjusted r^2..............: 0.029658
Sample size of AE DB......: 2421
Sample size of model......: 287
Missing data %............: 88.14539
- processing IL13_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Med.Statin.LLD + GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Med.Statin.LLDyes GFR_MDRD
0.964503 0.118751 -0.009798 -0.320600 -0.004803
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0126 -0.7298 -0.1381 0.4686 3.2438
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.988198 1.094849 0.903 0.36732
currentDF[, TRAIT] 0.103312 0.056624 1.825 0.06887 .
Age -0.010663 0.006996 -1.524 0.12832
Gendermale 0.104613 0.121231 0.863 0.38873
Hypertension.compositeyes -0.064382 0.164812 -0.391 0.69629
DiabetesStatusDiabetes 0.074290 0.138155 0.538 0.59108
SmokerStatusEx-smoker -0.073435 0.123146 -0.596 0.55132
SmokerStatusNever smoked 0.151039 0.188865 0.800 0.42438
Med.Statin.LLDyes -0.338706 0.126468 -2.678 0.00773 **
Med.all.antiplateletyes -0.118065 0.194270 -0.608 0.54373
GFR_MDRD -0.004081 0.003105 -1.314 0.18958
BMI -0.016222 0.015022 -1.080 0.28089
MedHx_CVDyes 0.123395 0.114855 1.074 0.28336
stenose50-70% -0.078735 0.821047 -0.096 0.92365
stenose70-90% 0.524287 0.771531 0.680 0.49721
stenose90-99% 0.435535 0.768951 0.566 0.57146
stenose100% (Occlusion) -0.282454 0.950979 -0.297 0.76662
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.071 on 376 degrees of freedom
Multiple R-squared: 0.06264, Adjusted R-squared: 0.02275
F-statistic: 1.57 on 16 and 376 DF, p-value: 0.07396
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL13_rank
Effect size...............: 0.103312
Standard error............: 0.056624
Odds ratio (effect size)..: 1.109
Lower 95% CI..............: 0.992
Upper 95% CI..............: 1.239
T-value...................: 1.82453
P-value...................: 0.06886544
R^2.......................: 0.06264
Adjusted r^2..............: 0.022753
Sample size of AE DB......: 2421
Sample size of model......: 393
Missing data %............: 83.76704
- processing IL21_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Med.Statin.LLD + GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Med.Statin.LLDyes GFR_MDRD
0.977989 0.117809 -0.010001 -0.319791 -0.004799
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0032 -0.7404 -0.1423 0.4567 3.2706
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.031232 1.093286 0.943 0.34616
currentDF[, TRAIT] 0.102924 0.057311 1.796 0.07331 .
Age -0.010760 0.006959 -1.546 0.12286
Gendermale 0.098167 0.120932 0.812 0.41745
Hypertension.compositeyes -0.070588 0.164409 -0.429 0.66792
DiabetesStatusDiabetes 0.067436 0.137810 0.489 0.62488
SmokerStatusEx-smoker -0.068904 0.122721 -0.561 0.57481
SmokerStatusNever smoked 0.152576 0.188483 0.809 0.41874
Med.Statin.LLDyes -0.340013 0.125994 -2.699 0.00728 **
Med.all.antiplateletyes -0.115920 0.193994 -0.598 0.55050
GFR_MDRD -0.004082 0.003101 -1.316 0.18893
BMI -0.016690 0.014994 -1.113 0.26637
MedHx_CVDyes 0.123932 0.114391 1.083 0.27932
stenose50-70% -0.100223 0.820921 -0.122 0.90290
stenose70-90% 0.509454 0.771305 0.661 0.50933
stenose90-99% 0.420669 0.768857 0.547 0.58461
stenose100% (Occlusion) -0.320906 0.950217 -0.338 0.73576
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.07 on 377 degrees of freedom
Multiple R-squared: 0.06236, Adjusted R-squared: 0.02256
F-statistic: 1.567 on 16 and 377 DF, p-value: 0.07492
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL21_rank
Effect size...............: 0.102924
Standard error............: 0.057311
Odds ratio (effect size)..: 1.108
Lower 95% CI..............: 0.991
Upper 95% CI..............: 1.24
T-value...................: 1.795899
P-value...................: 0.07331124
R^2.......................: 0.062357
Adjusted r^2..............: 0.022563
Sample size of AE DB......: 2421
Sample size of model......: 394
Missing data %............: 83.72573
- processing INFG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
SmokerStatus + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale SmokerStatusEx-smoker SmokerStatusNever smoked Med.Statin.LLDyes
-0.04532 0.13160 0.21032 -0.20798 0.17441 -0.27012
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8780 -0.7011 -0.1219 0.5258 3.2096
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.345506 1.176686 0.294 0.7693
currentDF[, TRAIT] 0.129547 0.068670 1.887 0.0602 .
Age -0.004679 0.007940 -0.589 0.5561
Gendermale 0.237954 0.138562 1.717 0.0870 .
Hypertension.compositeyes -0.258138 0.191356 -1.349 0.1784
DiabetesStatusDiabetes 0.002097 0.155583 0.013 0.9893
SmokerStatusEx-smoker -0.185635 0.137205 -1.353 0.1771
SmokerStatusNever smoked 0.274468 0.221640 1.238 0.2166
Med.Statin.LLDyes -0.285792 0.142507 -2.005 0.0458 *
Med.all.antiplateletyes -0.095334 0.217043 -0.439 0.6608
GFR_MDRD -0.002449 0.003548 -0.690 0.4906
BMI -0.005831 0.016991 -0.343 0.7317
MedHx_CVDyes 0.129744 0.132430 0.980 0.3280
stenose50-70% -0.178137 0.839176 -0.212 0.8320
stenose70-90% 0.530370 0.768914 0.690 0.4909
stenose90-99% 0.443557 0.764178 0.580 0.5621
stenose100% (Occlusion) -0.140367 1.101718 -0.127 0.8987
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.054 on 289 degrees of freedom
Multiple R-squared: 0.07563, Adjusted R-squared: 0.02445
F-statistic: 1.478 on 16 and 289 DF, p-value: 0.1067
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: INFG_rank
Effect size...............: 0.129547
Standard error............: 0.06867
Odds ratio (effect size)..: 1.138
Lower 95% CI..............: 0.995
Upper 95% CI..............: 1.302
T-value...................: 1.886511
P-value...................: 0.06022857
R^2.......................: 0.075628
Adjusted r^2..............: 0.024452
Sample size of AE DB......: 2421
Sample size of model......: 306
Missing data %............: 87.3606
- processing TNFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) Med.Statin.LLDyes
0.03563 -0.28879
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9968 -0.6898 -0.1119 0.5027 3.1775
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.2410441 0.9411949 -0.256 0.7981
currentDF[, TRAIT] 0.0769762 0.0635463 1.211 0.2269
Age -0.0020374 0.0079428 -0.257 0.7978
Gendermale 0.1919003 0.1383534 1.387 0.1666
Hypertension.compositeyes -0.2743491 0.1911496 -1.435 0.1524
DiabetesStatusDiabetes -0.1256775 0.1581838 -0.795 0.4276
SmokerStatusEx-smoker -0.1322331 0.1401695 -0.943 0.3464
SmokerStatusNever smoked 0.3171643 0.2240079 1.416 0.1580
Med.Statin.LLDyes -0.2636542 0.1394139 -1.891 0.0597 .
Med.all.antiplateletyes -0.0168753 0.2383434 -0.071 0.9436
GFR_MDRD -0.0007049 0.0036888 -0.191 0.8486
BMI -0.0067393 0.0187438 -0.360 0.7195
MedHx_CVDyes 0.1308073 0.1334987 0.980 0.3281
stenose70-90% 0.7836166 0.3472010 2.257 0.0248 *
stenose90-99% 0.7406320 0.3410943 2.171 0.0308 *
stenose100% (Occlusion) 0.1789049 0.8398722 0.213 0.8315
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.01 on 263 degrees of freedom
Multiple R-squared: 0.06963, Adjusted R-squared: 0.01657
F-statistic: 1.312 on 15 and 263 DF, p-value: 0.1943
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: TNFA_rank
Effect size...............: 0.076976
Standard error............: 0.063546
Odds ratio (effect size)..: 1.08
Lower 95% CI..............: 0.954
Upper 95% CI..............: 1.223
T-value...................: 1.21134
P-value...................: 0.2268525
R^2.......................: 0.06963
Adjusted r^2..............: 0.016567
Sample size of AE DB......: 2421
Sample size of model......: 279
Missing data %............: 88.47584
- processing MIF_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Med.Statin.LLDyes
-0.0688 0.2071 -0.2772
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8087 -0.7132 -0.1529 0.5068 3.2120
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.054350 1.080507 0.976 0.32979
currentDF[, TRAIT] 0.201269 0.057412 3.506 0.00051 ***
Age -0.009577 0.006863 -1.395 0.16371
Gendermale 0.093257 0.119079 0.783 0.43403
Hypertension.compositeyes -0.056140 0.162542 -0.345 0.72999
DiabetesStatusDiabetes 0.131902 0.137680 0.958 0.33866
SmokerStatusEx-smoker -0.004499 0.121789 -0.037 0.97055
SmokerStatusNever smoked 0.255062 0.185645 1.374 0.17028
Med.Statin.LLDyes -0.321880 0.124618 -2.583 0.01017 *
Med.all.antiplateletyes -0.164964 0.192404 -0.857 0.39178
GFR_MDRD -0.002749 0.003095 -0.888 0.37495
BMI -0.021075 0.014851 -1.419 0.15667
MedHx_CVDyes 0.098046 0.113354 0.865 0.38761
stenose50-70% -0.156727 0.810407 -0.193 0.84676
stenose70-90% 0.416527 0.761906 0.547 0.58491
stenose90-99% 0.303059 0.759791 0.399 0.69021
stenose100% (Occlusion) -0.436375 0.939870 -0.464 0.64271
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.057 on 377 degrees of freedom
Multiple R-squared: 0.08419, Adjusted R-squared: 0.04532
F-statistic: 2.166 on 16 and 377 DF, p-value: 0.005844
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MIF_rank
Effect size...............: 0.201269
Standard error............: 0.057412
Odds ratio (effect size)..: 1.223
Lower 95% CI..............: 1.093
Upper 95% CI..............: 1.369
T-value...................: 3.505686
P-value...................: 0.0005102625
R^2.......................: 0.08419
Adjusted r^2..............: 0.045322
Sample size of AE DB......: 2421
Sample size of model......: 394
Missing data %............: 83.72573
- processing MCP1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Med.Statin.LLD + GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Med.Statin.LLDyes GFR_MDRD
0.887810 0.271500 -0.009283 -0.264622 -0.004857
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2445 -0.6366 -0.1189 0.5190 2.9163
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.649319 1.068337 0.608 0.544
currentDF[, TRAIT] 0.262656 0.052933 4.962 1.06e-06 ***
Age -0.010184 0.006802 -1.497 0.135
Gendermale 0.071277 0.118586 0.601 0.548
Hypertension.compositeyes -0.041172 0.160232 -0.257 0.797
DiabetesStatusDiabetes 0.145833 0.135771 1.074 0.283
SmokerStatusEx-smoker -0.041040 0.119621 -0.343 0.732
SmokerStatusNever smoked 0.174445 0.182174 0.958 0.339
Med.Statin.LLDyes -0.283983 0.123431 -2.301 0.022 *
Med.all.antiplateletyes -0.075796 0.193772 -0.391 0.696
GFR_MDRD -0.004053 0.003024 -1.340 0.181
BMI -0.012800 0.014682 -0.872 0.384
MedHx_CVDyes 0.090957 0.112113 0.811 0.418
stenose50-70% 0.051541 0.795189 0.065 0.948
stenose70-90% 0.621010 0.746468 0.832 0.406
stenose90-99% 0.569344 0.743472 0.766 0.444
stenose100% (Occlusion) 0.063518 0.924900 0.069 0.945
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.039 on 373 degrees of freedom
Multiple R-squared: 0.1144, Adjusted R-squared: 0.07638
F-statistic: 3.01 on 16 and 373 DF, p-value: 9.262e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MCP1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MCP1_rank
Effect size...............: 0.262656
Standard error............: 0.052933
Odds ratio (effect size)..: 1.3
Lower 95% CI..............: 1.172
Upper 95% CI..............: 1.443
T-value...................: 4.962062
P-value...................: 1.062343e-06
R^2.......................: 0.114367
Adjusted r^2..............: 0.076378
Sample size of AE DB......: 2421
Sample size of model......: 390
Missing data %............: 83.89095
- processing MIP1a_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Med.Statin.LLD + GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Med.Statin.LLDyes GFR_MDRD
1.236175 0.131972 -0.012854 -0.390998 -0.005183
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9837 -0.7244 -0.1444 0.4723 3.3736
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.303368 1.137919 1.145 0.25285
currentDF[, TRAIT] 0.127706 0.060926 2.096 0.03681 *
Age -0.012736 0.007506 -1.697 0.09065 .
Gendermale 0.107920 0.131338 0.822 0.41183
Hypertension.compositeyes -0.159046 0.177538 -0.896 0.37097
DiabetesStatusDiabetes 0.172815 0.149709 1.154 0.24917
SmokerStatusEx-smoker -0.072837 0.132953 -0.548 0.58416
SmokerStatusNever smoked 0.133005 0.198434 0.670 0.50314
Med.Statin.LLDyes -0.408097 0.135686 -3.008 0.00283 **
Med.all.antiplateletyes -0.081988 0.218015 -0.376 0.70710
GFR_MDRD -0.004444 0.003243 -1.370 0.17155
BMI -0.015436 0.015863 -0.973 0.33121
MedHx_CVDyes 0.129081 0.124117 1.040 0.29908
stenose50-70% -0.135584 0.846966 -0.160 0.87291
stenose70-90% 0.392779 0.789229 0.498 0.61904
stenose90-99% 0.334327 0.785771 0.425 0.67076
stenose100% (Occlusion) -0.371391 0.973883 -0.381 0.70318
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.092 on 340 degrees of freedom
Multiple R-squared: 0.07594, Adjusted R-squared: 0.03245
F-statistic: 1.746 on 16 and 340 DF, p-value: 0.03725
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MIP1a_rank
Effect size...............: 0.127706
Standard error............: 0.060926
Odds ratio (effect size)..: 1.136
Lower 95% CI..............: 1.008
Upper 95% CI..............: 1.28
T-value...................: 2.096084
P-value...................: 0.03681363
R^2.......................: 0.07594
Adjusted r^2..............: 0.032454
Sample size of AE DB......: 2421
Sample size of model......: 357
Missing data %............: 85.25403
- processing RANTES_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Med.Statin.LLD + GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Med.Statin.LLDyes GFR_MDRD
0.937920 0.139942 -0.009339 -0.303461 -0.005060
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.84013 -0.70001 -0.09812 0.51104 3.12078
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.032069 1.093300 0.944 0.34579
currentDF[, TRAIT] 0.151617 0.055684 2.723 0.00678 **
Age -0.010713 0.006973 -1.536 0.12530
Gendermale 0.102464 0.122244 0.838 0.40246
Hypertension.compositeyes -0.119339 0.166402 -0.717 0.47372
DiabetesStatusDiabetes 0.156773 0.140755 1.114 0.26609
SmokerStatusEx-smoker -0.013911 0.123242 -0.113 0.91019
SmokerStatusNever smoked 0.230694 0.187271 1.232 0.21878
Med.Statin.LLDyes -0.329808 0.126887 -2.599 0.00972 **
Med.all.antiplateletyes -0.050260 0.199197 -0.252 0.80094
GFR_MDRD -0.004449 0.003109 -1.431 0.15336
BMI -0.019782 0.015087 -1.311 0.19061
MedHx_CVDyes 0.123071 0.115904 1.062 0.28900
stenose50-70% -0.116765 0.820489 -0.142 0.88691
stenose70-90% 0.504987 0.766700 0.659 0.51053
stenose90-99% 0.455322 0.763210 0.597 0.55115
stenose100% (Occlusion) -0.430368 0.950207 -0.453 0.65087
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.065 on 370 degrees of freedom
Multiple R-squared: 0.07536, Adjusted R-squared: 0.03538
F-statistic: 1.885 on 16 and 370 DF, p-value: 0.02057
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: RANTES_rank
Effect size...............: 0.151617
Standard error............: 0.055684
Odds ratio (effect size)..: 1.164
Lower 95% CI..............: 1.043
Upper 95% CI..............: 1.298
T-value...................: 2.722792
P-value...................: 0.006779779
R^2.......................: 0.075365
Adjusted r^2..............: 0.035381
Sample size of AE DB......: 2421
Sample size of model......: 387
Missing data %............: 84.01487
- processing MIG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Med.Statin.LLD + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) Age Med.Statin.LLDyes GFR_MDRD
1.245666 -0.013019 -0.341365 -0.005425
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9508 -0.7029 -0.1107 0.5033 3.2417
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.212729 1.113390 1.089 0.27677
currentDF[, TRAIT] -0.010196 0.058376 -0.175 0.86144
Age -0.013985 0.007161 -1.953 0.05159 .
Gendermale 0.103879 0.123988 0.838 0.40268
Hypertension.compositeyes -0.084696 0.168769 -0.502 0.61608
DiabetesStatusDiabetes 0.100572 0.142223 0.707 0.47993
SmokerStatusEx-smoker -0.034110 0.126262 -0.270 0.78720
SmokerStatusNever smoked 0.158732 0.195464 0.812 0.41728
Med.Statin.LLDyes -0.363626 0.128925 -2.820 0.00506 **
Med.all.antiplateletyes -0.119901 0.199244 -0.602 0.54769
GFR_MDRD -0.004730 0.003151 -1.501 0.13421
BMI -0.016765 0.015269 -1.098 0.27294
MedHx_CVDyes 0.100026 0.118091 0.847 0.39754
stenose50-70% -0.024618 0.833137 -0.030 0.97644
stenose70-90% 0.604029 0.778402 0.776 0.43826
stenose90-99% 0.537696 0.775346 0.693 0.48844
stenose100% (Occlusion) -0.291927 0.961227 -0.304 0.76153
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.081 on 366 degrees of freedom
Multiple R-squared: 0.05616, Adjusted R-squared: 0.01489
F-statistic: 1.361 on 16 and 366 DF, p-value: 0.1582
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MIG_rank
Effect size...............: -0.010196
Standard error............: 0.058376
Odds ratio (effect size)..: 0.99
Lower 95% CI..............: 0.883
Upper 95% CI..............: 1.11
T-value...................: -0.174664
P-value...................: 0.8614404
R^2.......................: 0.056155
Adjusted r^2..............: 0.014894
Sample size of AE DB......: 2421
Sample size of model......: 383
Missing data %............: 84.18009
- processing IP10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD +
MedHx_CVD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Med.Statin.LLDyes MedHx_CVDyes
-0.1740 0.1632 -0.3386 0.2106
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9485 -0.7086 -0.1010 0.5620 3.1718
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.834081 1.134311 0.735 0.4627
currentDF[, TRAIT] 0.162891 0.059370 2.744 0.0064 **
Age -0.008809 0.007511 -1.173 0.2417
Gendermale 0.116944 0.128085 0.913 0.3619
Hypertension.compositeyes -0.141101 0.174465 -0.809 0.4192
DiabetesStatusDiabetes 0.167958 0.148935 1.128 0.2602
SmokerStatusEx-smoker -0.095488 0.131837 -0.724 0.4694
SmokerStatusNever smoked -0.040579 0.203609 -0.199 0.8421
Med.Statin.LLDyes -0.375825 0.133605 -2.813 0.0052 **
Med.all.antiplateletyes -0.134307 0.203616 -0.660 0.5100
GFR_MDRD -0.005065 0.003257 -1.555 0.1209
BMI -0.010462 0.015801 -0.662 0.5084
MedHx_CVDyes 0.213841 0.121966 1.753 0.0805 .
stenose50-70% -0.078219 0.824424 -0.095 0.9245
stenose70-90% 0.510502 0.771500 0.662 0.5086
stenose90-99% 0.410846 0.767642 0.535 0.5929
stenose100% (Occlusion) -0.155864 0.952645 -0.164 0.8701
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.07 on 333 degrees of freedom
Multiple R-squared: 0.08354, Adjusted R-squared: 0.03951
F-statistic: 1.897 on 16 and 333 DF, p-value: 0.01987
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IP10_rank
Effect size...............: 0.162891
Standard error............: 0.05937
Odds ratio (effect size)..: 1.177
Lower 95% CI..............: 1.048
Upper 95% CI..............: 1.322
T-value...................: 2.743673
P-value...................: 0.00640467
R^2.......................: 0.083545
Adjusted r^2..............: 0.039511
Sample size of AE DB......: 2421
Sample size of model......: 350
Missing data %............: 85.54316
- processing Eotaxin1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Med.Statin.LLD + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) Age Med.Statin.LLDyes GFR_MDRD
1.088988 -0.011411 -0.319146 -0.004988
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9955 -0.7281 -0.1309 0.4653 3.2617
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.038025 1.097857 0.946 0.34501
currentDF[, TRAIT] 0.027195 0.058583 0.464 0.64276
Age -0.012122 0.006959 -1.742 0.08232 .
Gendermale 0.113409 0.122077 0.929 0.35348
Hypertension.compositeyes -0.079054 0.164995 -0.479 0.63212
DiabetesStatusDiabetes 0.060665 0.138308 0.439 0.66118
SmokerStatusEx-smoker -0.057817 0.123264 -0.469 0.63930
SmokerStatusNever smoked 0.181883 0.189737 0.959 0.33837
Med.Statin.LLDyes -0.339102 0.126496 -2.681 0.00767 **
Med.all.antiplateletyes -0.106585 0.194788 -0.547 0.58457
GFR_MDRD -0.004253 0.003115 -1.365 0.17298
BMI -0.017199 0.015055 -1.142 0.25399
MedHx_CVDyes 0.126692 0.114867 1.103 0.27076
stenose50-70% -0.018179 0.824248 -0.022 0.98242
stenose70-90% 0.595972 0.774390 0.770 0.44202
stenose90-99% 0.516781 0.771815 0.670 0.50354
stenose100% (Occlusion) -0.288566 0.954406 -0.302 0.76255
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.074 on 377 degrees of freedom
Multiple R-squared: 0.05488, Adjusted R-squared: 0.01476
F-statistic: 1.368 on 16 and 377 DF, p-value: 0.1542
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 0.027195
Standard error............: 0.058583
Odds ratio (effect size)..: 1.028
Lower 95% CI..............: 0.916
Upper 95% CI..............: 1.153
T-value...................: 0.464221
P-value...................: 0.6427576
R^2.......................: 0.054875
Adjusted r^2..............: 0.014764
Sample size of AE DB......: 2421
Sample size of model......: 394
Missing data %............: 83.72573
- processing TARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Med.Statin.LLD + GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Med.Statin.LLDyes GFR_MDRD
1.230714 0.180738 -0.012472 -0.350318 -0.006449
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9124 -0.6663 -0.0809 0.4529 3.4131
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.653635 1.129639 1.464 0.14422
currentDF[, TRAIT] 0.170766 0.060421 2.826 0.00501 **
Age -0.012940 0.007286 -1.776 0.07670 .
Gendermale 0.094517 0.127846 0.739 0.46027
Hypertension.compositeyes -0.198279 0.172386 -1.150 0.25092
DiabetesStatusDiabetes 0.136319 0.148120 0.920 0.35810
SmokerStatusEx-smoker -0.044670 0.130880 -0.341 0.73310
SmokerStatusNever smoked 0.201623 0.193233 1.043 0.29754
Med.Statin.LLDyes -0.368263 0.137363 -2.681 0.00772 **
Med.all.antiplateletyes -0.060989 0.207130 -0.294 0.76861
GFR_MDRD -0.006674 0.003355 -1.989 0.04753 *
BMI -0.022655 0.016203 -1.398 0.16304
MedHx_CVDyes 0.080311 0.123200 0.652 0.51495
stenose50-70% -0.110835 0.813247 -0.136 0.89168
stenose70-90% 0.373469 0.758142 0.493 0.62263
stenose90-99% 0.311653 0.754911 0.413 0.68001
stenose100% (Occlusion) -0.361833 0.937631 -0.386 0.69983
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.051 on 319 degrees of freedom
Multiple R-squared: 0.09163, Adjusted R-squared: 0.04607
F-statistic: 2.011 on 16 and 319 DF, p-value: 0.01224
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: TARC_rank
Effect size...............: 0.170766
Standard error............: 0.060421
Odds ratio (effect size)..: 1.186
Lower 95% CI..............: 1.054
Upper 95% CI..............: 1.335
T-value...................: 2.826284
P-value...................: 0.005006253
R^2.......................: 0.091634
Adjusted r^2..............: 0.046073
Sample size of AE DB......: 2421
Sample size of model......: 336
Missing data %............: 86.12144
- processing PARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Med.Statin.LLDyes
-0.07028 0.20862 -0.27610
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8915 -0.7156 -0.1147 0.5200 3.0281
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.764232 1.084946 0.704 0.481621
currentDF[, TRAIT] 0.186416 0.055833 3.339 0.000925 ***
Age -0.010738 0.006843 -1.569 0.117435
Gendermale 0.122878 0.118973 1.033 0.302346
Hypertension.compositeyes -0.034904 0.163201 -0.214 0.830762
DiabetesStatusDiabetes 0.115286 0.137344 0.839 0.401776
SmokerStatusEx-smoker -0.039201 0.121239 -0.323 0.746618
SmokerStatusNever smoked 0.197144 0.185114 1.065 0.287563
Med.Statin.LLDyes -0.324616 0.124779 -2.602 0.009647 **
Med.all.antiplateletyes -0.069714 0.192129 -0.363 0.716922
GFR_MDRD -0.003449 0.003078 -1.121 0.263151
BMI -0.018064 0.014837 -1.218 0.224168
MedHx_CVDyes 0.109304 0.113335 0.964 0.335446
stenose50-70% 0.120849 0.810850 0.149 0.881602
stenose70-90% 0.637147 0.760674 0.838 0.402781
stenose90-99% 0.544139 0.757639 0.718 0.473076
stenose100% (Occlusion) -0.047844 0.942407 -0.051 0.959537
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.059 on 377 degrees of freedom
Multiple R-squared: 0.08149, Adjusted R-squared: 0.04251
F-statistic: 2.091 on 16 and 377 DF, p-value: 0.008255
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: PARC_rank
Effect size...............: 0.186416
Standard error............: 0.055833
Odds ratio (effect size)..: 1.205
Lower 95% CI..............: 1.08
Upper 95% CI..............: 1.344
T-value...................: 3.338792
P-value...................: 0.0009253009
R^2.......................: 0.081494
Adjusted r^2..............: 0.042513
Sample size of AE DB......: 2421
Sample size of model......: 394
Missing data %............: 83.72573
- processing MDC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Med.Statin.LLD + GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Med.Statin.LLDyes GFR_MDRD
1.272743 0.158371 -0.013009 -0.399520 -0.005476
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9638 -0.6982 -0.1252 0.5091 3.2758
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.457356 1.134077 1.285 0.19965
currentDF[, TRAIT] 0.159091 0.060418 2.633 0.00885 **
Age -0.013851 0.007378 -1.877 0.06134 .
Gendermale 0.133867 0.130590 1.025 0.30606
Hypertension.compositeyes -0.164649 0.178053 -0.925 0.35577
DiabetesStatusDiabetes 0.214873 0.151813 1.415 0.15788
SmokerStatusEx-smoker -0.023411 0.131993 -0.177 0.85933
SmokerStatusNever smoked 0.160679 0.196933 0.816 0.41513
Med.Statin.LLDyes -0.427670 0.134004 -3.191 0.00155 **
Med.all.antiplateletyes -0.065266 0.221580 -0.295 0.76852
GFR_MDRD -0.004886 0.003275 -1.492 0.13667
BMI -0.017952 0.015874 -1.131 0.25892
MedHx_CVDyes 0.146186 0.123938 1.180 0.23903
stenose50-70% -0.149530 0.843365 -0.177 0.85938
stenose70-90% 0.362155 0.786393 0.461 0.64544
stenose90-99% 0.282146 0.783403 0.360 0.71896
stenose100% (Occlusion) -0.408052 0.970377 -0.421 0.67438
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.088 on 337 degrees of freedom
Multiple R-squared: 0.087, Adjusted R-squared: 0.04365
F-statistic: 2.007 on 16 and 337 DF, p-value: 0.01231
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MDC_rank
Effect size...............: 0.159091
Standard error............: 0.060418
Odds ratio (effect size)..: 1.172
Lower 95% CI..............: 1.042
Upper 95% CI..............: 1.32
T-value...................: 2.633155
P-value...................: 0.008849335
R^2.......................: 0.087
Adjusted r^2..............: 0.043653
Sample size of AE DB......: 2421
Sample size of model......: 354
Missing data %............: 85.37794
- processing OPG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Med.Statin.LLD + GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Med.Statin.LLDyes GFR_MDRD
0.998102 0.189516 -0.009877 -0.314467 -0.005257
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7655 -0.6928 -0.1118 0.5148 3.1615
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.870125 1.084450 0.802 0.422848
currentDF[, TRAIT] 0.181413 0.054163 3.349 0.000892 ***
Age -0.010456 0.006880 -1.520 0.129378
Gendermale 0.082936 0.120175 0.690 0.490542
Hypertension.compositeyes -0.031125 0.163556 -0.190 0.849176
DiabetesStatusDiabetes 0.096485 0.136953 0.705 0.481551
SmokerStatusEx-smoker -0.084674 0.121769 -0.695 0.487258
SmokerStatusNever smoked 0.148777 0.185920 0.800 0.424088
Med.Statin.LLDyes -0.330477 0.125197 -2.640 0.008645 **
Med.all.antiplateletyes -0.149109 0.192599 -0.774 0.439303
GFR_MDRD -0.004349 0.003070 -1.417 0.157436
BMI -0.014832 0.014874 -0.997 0.319323
MedHx_CVDyes 0.123154 0.113653 1.084 0.279239
stenose50-70% 0.037336 0.811202 0.046 0.963315
stenose70-90% 0.616899 0.761605 0.810 0.418454
stenose90-99% 0.531979 0.758561 0.701 0.483550
stenose100% (Occlusion) -0.063606 0.943218 -0.067 0.946271
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.06 on 376 degrees of freedom
Multiple R-squared: 0.08174, Adjusted R-squared: 0.04266
F-statistic: 2.092 on 16 and 376 DF, p-value: 0.008213
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: OPG_rank
Effect size...............: 0.181413
Standard error............: 0.054163
Odds ratio (effect size)..: 1.199
Lower 95% CI..............: 1.078
Upper 95% CI..............: 1.333
T-value...................: 3.349384
P-value...................: 0.0008918522
R^2.......................: 0.081739
Adjusted r^2..............: 0.042664
Sample size of AE DB......: 2421
Sample size of model......: 393
Missing data %............: 83.76704
- processing sICAM1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Med.Statin.LLDyes
-0.07021 0.16636 -0.27053
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9402 -0.6903 -0.1354 0.5022 3.1009
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.901026 1.089216 0.827 0.40863
currentDF[, TRAIT] 0.143573 0.054927 2.614 0.00931 **
Age -0.009282 0.006968 -1.332 0.18367
Gendermale 0.113172 0.119686 0.946 0.34497
Hypertension.compositeyes -0.077803 0.163550 -0.476 0.63456
DiabetesStatusDiabetes 0.096341 0.137810 0.699 0.48493
SmokerStatusEx-smoker -0.046386 0.121873 -0.381 0.70371
SmokerStatusNever smoked 0.178301 0.186253 0.957 0.33903
Med.Statin.LLDyes -0.314030 0.125774 -2.497 0.01296 *
Med.all.antiplateletyes -0.104653 0.192948 -0.542 0.58787
GFR_MDRD -0.003602 0.003096 -1.163 0.24546
BMI -0.018641 0.014926 -1.249 0.21247
MedHx_CVDyes 0.099486 0.114356 0.870 0.38488
stenose50-70% -0.090352 0.815647 -0.111 0.91185
stenose70-90% 0.519829 0.766017 0.679 0.49780
stenose90-99% 0.426714 0.763337 0.559 0.57649
stenose100% (Occlusion) -0.263251 0.945326 -0.278 0.78080
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.065 on 377 degrees of freedom
Multiple R-squared: 0.07117, Adjusted R-squared: 0.03175
F-statistic: 1.805 on 16 and 377 DF, p-value: 0.02875
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: sICAM1_rank
Effect size...............: 0.143573
Standard error............: 0.054927
Odds ratio (effect size)..: 1.154
Lower 95% CI..............: 1.037
Upper 95% CI..............: 1.286
T-value...................: 2.613889
P-value...................: 0.009310471
R^2.......................: 0.071168
Adjusted r^2..............: 0.031748
Sample size of AE DB......: 2421
Sample size of model......: 394
Missing data %............: 83.72573
- processing VEGFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Med.Statin.LLD + GFR_MDRD +
BMI + stenose, data = currentDF)
Coefficients:
(Intercept) Age Med.Statin.LLDyes GFR_MDRD BMI stenose50-70% stenose70-90%
0.561958 -0.012663 -0.297486 -0.006419 -0.022865 0.569596 1.391138
stenose90-99% stenose100% (Occlusion)
1.216539 0.562266
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8596 -0.6291 -0.0838 0.4964 3.0093
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.607522 1.345220 0.452 0.6519
currentDF[, TRAIT] -0.003457 0.057353 -0.060 0.9520
Age -0.011991 0.007339 -1.634 0.1033
Gendermale 0.178602 0.128686 1.388 0.1662
Hypertension.compositeyes -0.163322 0.178819 -0.913 0.3618
DiabetesStatusDiabetes -0.084955 0.143970 -0.590 0.5556
SmokerStatusEx-smoker -0.129825 0.128693 -1.009 0.3139
SmokerStatusNever smoked 0.231649 0.203142 1.140 0.2550
Med.Statin.LLDyes -0.258439 0.132397 -1.952 0.0518 .
Med.all.antiplateletyes -0.182370 0.195220 -0.934 0.3509
GFR_MDRD -0.006750 0.003112 -2.169 0.0308 *
BMI -0.017707 0.016166 -1.095 0.2742
MedHx_CVDyes 0.039904 0.122523 0.326 0.7449
stenose50-70% 0.537706 1.093205 0.492 0.6232
stenose70-90% 1.365104 1.046768 1.304 0.1932
stenose90-99% 1.195350 1.044758 1.144 0.2534
stenose100% (Occlusion) 0.378078 1.178266 0.321 0.7485
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.036 on 313 degrees of freedom
Multiple R-squared: 0.0856, Adjusted R-squared: 0.03886
F-statistic: 1.831 on 16 and 313 DF, p-value: 0.02657
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: VEGFA_rank
Effect size...............: -0.003457
Standard error............: 0.057353
Odds ratio (effect size)..: 0.997
Lower 95% CI..............: 0.891
Upper 95% CI..............: 1.115
T-value...................: -0.060281
P-value...................: 0.9519706
R^2.......................: 0.085598
Adjusted r^2..............: 0.038856
Sample size of AE DB......: 2421
Sample size of model......: 330
Missing data %............: 86.36927
- processing TGFB_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Med.Statin.LLD + GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Med.Statin.LLDyes GFR_MDRD
1.075126 0.121559 -0.010900 -0.285644 -0.005516
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8652 -0.6771 -0.1268 0.4888 3.1195
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.020467 1.108745 0.920 0.3580
currentDF[, TRAIT] 0.131606 0.055751 2.361 0.0188 *
Age -0.012702 0.007078 -1.795 0.0735 .
Gendermale 0.124424 0.123639 1.006 0.3149
Hypertension.compositeyes -0.072924 0.166026 -0.439 0.6608
DiabetesStatusDiabetes 0.081260 0.139664 0.582 0.5610
SmokerStatusEx-smoker -0.018945 0.124477 -0.152 0.8791
SmokerStatusNever smoked 0.273180 0.191927 1.423 0.1555
Med.Statin.LLDyes -0.309384 0.128266 -2.412 0.0164 *
Med.all.antiplateletyes -0.056329 0.197713 -0.285 0.7759
GFR_MDRD -0.004745 0.003163 -1.500 0.1344
BMI -0.016197 0.015407 -1.051 0.2938
MedHx_CVDyes 0.125327 0.116593 1.075 0.2831
stenose50-70% -0.139359 0.821312 -0.170 0.8654
stenose70-90% 0.559599 0.773324 0.724 0.4698
stenose90-99% 0.483524 0.770196 0.628 0.5305
stenose100% (Occlusion) -0.348375 0.956225 -0.364 0.7158
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.075 on 367 degrees of freedom
Multiple R-squared: 0.07099, Adjusted R-squared: 0.03049
F-statistic: 1.753 on 16 and 367 DF, p-value: 0.03591
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: TGFB_rank
Effect size...............: 0.131606
Standard error............: 0.055751
Odds ratio (effect size)..: 1.141
Lower 95% CI..............: 1.023
Upper 95% CI..............: 1.272
T-value...................: 2.3606
P-value...................: 0.01876773
R^2.......................: 0.07099
Adjusted r^2..............: 0.030489
Sample size of AE DB......: 2421
Sample size of model......: 384
Missing data %............: 84.13879
- processing MMP2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Med.Statin.LLD + GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale Med.Statin.LLDyes GFR_MDRD
0.860714 0.208459 -0.009766 0.180657 -0.265555 -0.005786
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2294 -0.7016 -0.1237 0.5027 3.0624
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.789092 1.102062 0.716 0.47443
currentDF[, TRAIT] 0.197640 0.059853 3.302 0.00105 **
Age -0.011113 0.006940 -1.601 0.11012
Gendermale 0.208279 0.121321 1.717 0.08686 .
Hypertension.compositeyes -0.010045 0.169165 -0.059 0.95268
DiabetesStatusDiabetes 0.021362 0.137102 0.156 0.87627
SmokerStatusEx-smoker -0.070769 0.122503 -0.578 0.56382
SmokerStatusNever smoked 0.212581 0.187413 1.134 0.25740
Med.Statin.LLDyes -0.276110 0.125543 -2.199 0.02847 *
Med.all.antiplateletyes -0.157313 0.198913 -0.791 0.42953
GFR_MDRD -0.005032 0.003069 -1.640 0.10190
BMI -0.016958 0.015325 -1.107 0.26921
MedHx_CVDyes 0.133523 0.115436 1.157 0.24814
stenose50-70% 0.105197 0.813586 0.129 0.89719
stenose70-90% 0.684198 0.766829 0.892 0.37284
stenose90-99% 0.629968 0.763582 0.825 0.40989
stenose100% (Occlusion) -0.235865 0.947909 -0.249 0.80363
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.067 on 372 degrees of freedom
Multiple R-squared: 0.08797, Adjusted R-squared: 0.04874
F-statistic: 2.242 on 16 and 372 DF, p-value: 0.004115
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MMP2_rank
Effect size...............: 0.19764
Standard error............: 0.059853
Odds ratio (effect size)..: 1.219
Lower 95% CI..............: 1.084
Upper 95% CI..............: 1.37
T-value...................: 3.302098
P-value...................: 0.001052585
R^2.......................: 0.087965
Adjusted r^2..............: 0.048738
Sample size of AE DB......: 2421
Sample size of model......: 389
Missing data %............: 83.93226
- processing MMP8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Med.Statin.LLD + GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Med.Statin.LLDyes GFR_MDRD
1.06102 0.14799 -0.01108 -0.26693 -0.00553
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1959 -0.7152 -0.1282 0.5110 3.2091
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.482771 1.108685 1.337 0.1819
currentDF[, TRAIT] 0.147885 0.058910 2.510 0.0125 *
Age -0.013465 0.006936 -1.941 0.0530 .
Gendermale 0.108072 0.123868 0.872 0.3835
Hypertension.compositeyes -0.128748 0.167537 -0.768 0.4427
DiabetesStatusDiabetes 0.042865 0.138536 0.309 0.7572
SmokerStatusEx-smoker -0.026843 0.123288 -0.218 0.8278
SmokerStatusNever smoked 0.297465 0.187586 1.586 0.1136
Med.Statin.LLDyes -0.281276 0.126283 -2.227 0.0265 *
Med.all.antiplateletyes -0.073495 0.200015 -0.367 0.7135
GFR_MDRD -0.005165 0.003091 -1.671 0.0956 .
BMI -0.024522 0.015541 -1.578 0.1155
MedHx_CVDyes 0.149911 0.115941 1.293 0.1968
stenose50-70% -0.212904 0.823889 -0.258 0.7962
stenose70-90% 0.395814 0.778460 0.508 0.6114
stenose90-99% 0.375920 0.773158 0.486 0.6271
stenose100% (Occlusion) -0.544177 0.961255 -0.566 0.5717
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.073 on 372 degrees of freedom
Multiple R-squared: 0.07687, Adjusted R-squared: 0.03717
F-statistic: 1.936 on 16 and 372 DF, p-value: 0.01647
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MMP8_rank
Effect size...............: 0.147885
Standard error............: 0.05891
Odds ratio (effect size)..: 1.159
Lower 95% CI..............: 1.033
Upper 95% CI..............: 1.301
T-value...................: 2.510372
P-value...................: 0.01248401
R^2.......................: 0.076871
Adjusted r^2..............: 0.037166
Sample size of AE DB......: 2421
Sample size of model......: 389
Missing data %............: 83.93226
- processing MMP9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Med.Statin.LLD + GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Med.Statin.LLDyes GFR_MDRD
1.04087 0.13159 -0.01111 -0.27097 -0.00525
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1475 -0.6729 -0.1143 0.4851 3.0766
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.248373 1.104524 1.130 0.2591
currentDF[, TRAIT] 0.124657 0.055755 2.236 0.0260 *
Age -0.013384 0.006949 -1.926 0.0549 .
Gendermale 0.133726 0.122695 1.090 0.2765
Hypertension.compositeyes -0.085614 0.168061 -0.509 0.6108
DiabetesStatusDiabetes 0.036571 0.138665 0.264 0.7921
SmokerStatusEx-smoker -0.033985 0.123377 -0.275 0.7831
SmokerStatusNever smoked 0.273707 0.187789 1.458 0.1458
Med.Statin.LLDyes -0.288894 0.126441 -2.285 0.0229 *
Med.all.antiplateletyes -0.112078 0.199903 -0.561 0.5754
GFR_MDRD -0.004967 0.003113 -1.595 0.1115
BMI -0.023436 0.015539 -1.508 0.1323
MedHx_CVDyes 0.156463 0.116099 1.348 0.1786
stenose50-70% -0.010769 0.819811 -0.013 0.9895
stenose70-90% 0.591452 0.773337 0.765 0.4449
stenose90-99% 0.527537 0.770091 0.685 0.4937
stenose100% (Occlusion) -0.338213 0.956284 -0.354 0.7238
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.075 on 372 degrees of freedom
Multiple R-squared: 0.07368, Adjusted R-squared: 0.03384
F-statistic: 1.849 on 16 and 372 DF, p-value: 0.02393
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MMP9_rank
Effect size...............: 0.124657
Standard error............: 0.055755
Odds ratio (effect size)..: 1.133
Lower 95% CI..............: 1.015
Upper 95% CI..............: 1.264
T-value...................: 2.235786
P-value...................: 0.02595834
R^2.......................: 0.073679
Adjusted r^2..............: 0.033838
Sample size of AE DB......: 2421
Sample size of model......: 389
Missing data %............: 83.93226 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)DT::datatable(GLM.results)
# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.MCP1_Plaque.Cytokines_Plaques.RANK.MODEL2.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Session information
Version: v1.0.9
Last update: 2020-06-26
Written by: Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description: Script to analyse MCP1 from the Ather-Express Biobank Study.
Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
**MoSCoW To-Do List**
The things we Must, Should, Could, and Would have given the time we have.
_M_
* analysis on serum ASAP
* linear regression models (model 1 and model 2) of `MCP1_pg_ug_2015` with cytokines DONE
* check out the difference between the measuremens of `MCP1` and `MCP1_pg_ug_2015` MONDAY
* double check the plotting of the MACE DONE
* add the statistics for the correlation of `MCP1_pg_ug_2015` with the cytokines DONE
_S_
* prettify forest plot
_C_
_W_
**Changes log**
* v1.0.9 Added linear regression models for MCP1 vs. cytokines plaque levels. Double checked upload of MACE-plots. Added statistics from correlation (heatmap) to txt-file.
* v1.0.8 Fixed error in MCP1 serum analysis. It turns out the `MCP1` and `MCP1_pg_ug_2015` variables are _both_ measured in plaque, in two separate experiments, exp. no. 1 and exp. no. 2, respectively.
* v1.0.7 Fixed the per Age-group MCP1 Box plots. Added correlations with other cytokines in plaques.
* v1.0.6 Only analyses and figures that end up in the final manuscript.
* v1.0.5 Update with 30- and 90-days survival.
* v1.0.4 Updated with Cox-regressions.
* v1.0.3 Included more models.
* v1.0.2 Bugs fixed.
* v1.0.1 Extended with linear and logistic regressions
* v1.0.0 Inital versionsessionInfo()Saving environment
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".sample_selection.RData"))| © 1979-2020 Sander W. van der Laan | s.w.vanderlaan-2[at]umcutrecht.nl | swvanderlaan.github.io. |
---
title: "Athero-Express Biobank Study -- IL6 and MCP1 plaque levels."
author: '[Sander W. van der Laan, PhD](https://swvanderlaan.github.io) | @swvanderlaan; Marios Georgakis; Rainer Malik; Martin Dichgans'
date: '`r Sys.Date()`'
output:
  html_notebook:
    cache: yes
    code_folding: hide
    collapse: yes
    df_print: paged
    fig.align: center
    fig_caption: yes
    fig_height: 10
    fig_retina: 2
    fig_width: 12
    theme: paper
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: yes
mainfont: Helvetica
subtitle: An 'Athero-Express Biobank Study' project
editor_options:
  chunk_output_type: inline
---
```{r global_options, include = FALSE}
# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/',
                      eval = TRUE, warning = FALSE, message = FALSE)
```

# Preparation

Clean the environment.
```{r ClearEnvironment, include = FALSE}
rm(list = ls())
```

Set locations, and the working directory.
```{r LocalSystem, include = FALSE}
### Operating System Version
### Mac Pro
# ROOT_loc = "/Volumes/EliteProQx2Media"
# GENOMIC_loc = "/Users/svanderlaan/iCloud/Genomics"

### MacBook
ROOT_loc = "/Users/swvanderlaan"
GENOMIC_loc = paste0(ROOT_loc, "/iCloud/Genomics")

### Generic Locations
AEDB_loc = paste0(GENOMIC_loc, "/AE-AAA_GS_DBs")
LAB_loc = paste0(GENOMIC_loc, "/LabBusiness")
RESULTS = paste0(ROOT_loc, "/PLINK/analyses/lookups/AE_20190912_010_MDICHGANS_SWVDLAAN_IL6_MCP1")
RAWDATA = paste0(ROOT_loc, "/PLINK/_AE_ORIGINALS/AESCRNA/prepped_data")
PROJECT_loc = paste0(ROOT_loc, "/PLINK/analyses/lookups/AE_20190912_010_MDICHGANS_SWVDLAAN_IL6_MCP1")

### SOME VARIABLES WE NEED DOWN THE LINE
cat("\nDefining phenotypes and datasets.\n")
PROJECTNAME="IL6MCP1"
# SUBPROJECTNAME=""

cat("\nCreate a new analysis directory, including subdirectories.\n")
# Analysis
ifelse(!dir.exists(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       dir.create(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       FALSE)
ANALYSIS_loc = paste0(PROJECT_loc,"/",PROJECTNAME)

# Plots
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/PLOTS")), 
       dir.create(file.path(ANALYSIS_loc, "/PLOTS")), 
       FALSE)
PLOT_loc = paste0(ANALYSIS_loc,"/PLOTS")

# QC plots
ifelse(!dir.exists(file.path(PLOT_loc, "/QC")), 
       dir.create(file.path(PLOT_loc, "/QC")), 
       FALSE)
QC_loc = paste0(PLOT_loc,"/QC")

# Output files
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/OUTPUT")), 
       dir.create(file.path(ANALYSIS_loc, "/OUTPUT")), 
       FALSE)
OUT_loc = paste0(ANALYSIS_loc, "/OUTPUT")

# COX analysis
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/COX")), 
       dir.create(file.path(ANALYSIS_loc, "/COX")), 
       FALSE)
COX_loc = paste0(ANALYSIS_loc, "/COX")

# Baseline characteristics
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/BASELINE")), 
       dir.create(file.path(ANALYSIS_loc, "/BASELINE")), 
       FALSE)
BASELINE_loc = paste0(ANALYSIS_loc, "/BASELINE")

# Sample selection
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/SELECTIONS")), 
       dir.create(file.path(ANALYSIS_loc, "/SELECTIONS")), 
       FALSE)
SELECTIONS_loc = paste0(ANALYSIS_loc, "/SELECTIONS")

cat("\nSetting working directory and listing its contents.\n")
setwd(paste0(PROJECT_loc))
getwd()
list.files()
```

A package-installation function.
```{r Function: installations, include = FALSE}
install.packages.auto <- function(x) { 
  x <- as.character(substitute(x)) 
  if(isTRUE(x %in% .packages(all.available = TRUE))) { 
    eval(parse(text = sprintf("require(\"%s\")", x)))
  } else { 
    # Update installed packages - this may mean a full upgrade of R, which in turn
    # may not be warrented. 
    # update.install.packages.auto(ask = FALSE) 
    eval(parse(text = sprintf("install.packages(\"%s\", dependencies = TRUE, repos = \"https://cloud.r-project.org/\")", x)))
  }
  if(isTRUE(x %in% .packages(all.available = TRUE))) { 
    eval(parse(text = sprintf("require(\"%s\")", x)))
  } else {
    if (!requireNamespace("BiocManager"))
      install.packages("BiocManager")
    # BiocManager::install() # this would entail updating installed packages, which in turned may not be warrented
    eval(parse(text = sprintf("BiocManager::install(\"%s\")", x)))
    eval(parse(text = sprintf("require(\"%s\")", x)))
  }
}
```

Load those packages.
```{r Setting: loading_packages, include = FALSE}
install.packages.auto("readr")
install.packages.auto("optparse")
install.packages.auto("tools")
install.packages.auto("dplyr")
install.packages.auto("tidyr")
install.packages.auto("naniar")

# To get 'data.table' with 'fwrite' to be able to directly write gzipped-files
# Ref: https://stackoverflow.com/questions/42788401/is-possible-to-use-fwrite-from-data-table-with-gzfile
# install.packages("data.table", repos = "https://Rdatatable.gitlab.io/data.table")
library(data.table)

install.packages.auto("tidyverse")
install.packages.auto("knitr")
install.packages.auto("DT")
install.packages.auto("MASS")
# install.packages.auto("Seurat") # latest version

# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')

install.packages.auto("haven")
install.packages.auto("sjlabelled")
install.packages.auto("sjPlot")
install.packages.auto("labelled")
install.packages.auto("tableone")

install.packages.auto("ggpubr")

```

We will create a datestamp and define the Utrecht Science Park Colour Scheme.
```{r Setting: Colors, include = FALSE}

Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")

### UtrechtScienceParkColoursScheme
###
### WebsitetoconvertHEXtoRGB:http://hex.colorrrs.com.
### Forsomefunctionsyoushoulddividethesenumbersby255.
### 
###	No.	Color			      HEX	(RGB)						              CHR		  MAF/INFO
###---------------------------------------------------------------------------------------
###	1	  yellow			    #FBB820 (251,184,32)				      =>	1		or 1.0>INFO
###	2	  gold			      #F59D10 (245,157,16)				      =>	2		
###	3	  salmon			    #E55738 (229,87,56)				      =>	3		or 0.05<MAF<0.2 or 0.4<INFO<0.6
###	4	  darkpink		    #DB003F ((219,0,63)				      =>	4		
###	5	  lightpink		    #E35493 (227,84,147)				      =>	5		or 0.8<INFO<1.0
###	6	  pink			      #D5267B (213,38,123)				      =>	6		
###	7	  hardpink		    #CC0071 (204,0,113)				      =>	7		
###	8	  lightpurple	    #A8448A (168,68,138)				      =>	8		
###	9	  purple			    #9A3480 (154,52,128)				      =>	9		
###	10	lavendel		    #8D5B9A (141,91,154)				      =>	10		
###	11	bluepurple		  #705296 (112,82,150)				      =>	11		
###	12	purpleblue		  #686AA9 (104,106,169)			      =>	12		
###	13	lightpurpleblue	#6173AD (97,115,173/101,120,180)	=>	13		
###	14	seablue			    #4C81BF (76,129,191)				      =>	14		
###	15	skyblue			    #2F8BC9 (47,139,201)				      =>	15		
###	16	azurblue		    #1290D9 (18,144,217)				      =>	16		or 0.01<MAF<0.05 or 0.2<INFO<0.4
###	17	lightazurblue	  #1396D8 (19,150,216)				      =>	17		
###	18	greenblue		    #15A6C1 (21,166,193)				      =>	18		
###	19	seaweedgreen	  #5EB17F (94,177,127)				      =>	19		
###	20	yellowgreen		  #86B833 (134,184,51)				      =>	20		
###	21	lightmossgreen	#C5D220 (197,210,32)				      =>	21		
###	22	mossgreen		    #9FC228 (159,194,40)				      =>	22		or MAF>0.20 or 0.6<INFO<0.8
###	23	lightgreen	  	#78B113 (120,177,19)				      =>	23/X
###	24	green			      #49A01D (73,160,29)				      =>	24/Y
###	25	grey			      #595A5C (89,90,92)				        =>	25/XY	or MAF<0.01 or 0.0<INFO<0.2
###	26	lightgrey		    #A2A3A4	(162,163,164)			      =>	26/MT
###
###	ADDITIONAL COLORS
###	27	midgrey			#D7D8D7
###	28	verylightgrey	#ECECEC"
###	29	white			#FFFFFF
###	30	black			#000000
###----------------------------------------------------------------------------------------------

uithof_color = c("#FBB820","#F59D10","#E55738","#DB003F","#E35493","#D5267B",
                 "#CC0071","#A8448A","#9A3480","#8D5B9A","#705296","#686AA9",
                 "#6173AD","#4C81BF","#2F8BC9","#1290D9","#1396D8","#15A6C1",
                 "#5EB17F","#86B833","#C5D220","#9FC228","#78B113","#49A01D",
                 "#595A5C","#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

uithof_color_legend = c("#FBB820", "#F59D10", "#E55738", "#DB003F", "#E35493",
                        "#D5267B", "#CC0071", "#A8448A", "#9A3480", "#8D5B9A",
                        "#705296", "#686AA9", "#6173AD", "#4C81BF", "#2F8BC9",
                        "#1290D9", "#1396D8", "#15A6C1", "#5EB17F", "#86B833",
                        "#C5D220", "#9FC228", "#78B113", "#49A01D", "#595A5C",
                        "#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

#ggplot2 default color palette
gg_color_hue <- function(n) {
  hues = seq(15, 375, length = n + 1)
  hcl(h = hues, l = 65, c = 100)[1:n]
}

### ----------------------------------------------------------------------------
```


```{r Analysis Functions}
# Function to grep data from glm()/lm()
GLM.CON <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
  cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' .\n")
  if (nrow(summary(fit)$coefficients) == 1) {
    output = c(DATASET, x_name, y, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    effectsize = summary(fit)$coefficients[2,1]
    SE = summary(fit)$coefficients[2,2]
    OReffect = exp(summary(fit)$coefficients[2,1])
    CI_low = exp(effectsize - 1.96 * SE)
    CI_up = exp(effectsize + 1.96 * SE)
    tvalue = summary(fit)$coefficients[2,3]
    pvalue = summary(fit)$coefficients[2,4]
    R = summary(fit)$r.squared
    R.adj = summary(fit)$adj.r.squared
    sample_size = nrow(model.frame(fit))
    AE_N = AEDB.CEA.samplesize
    Perc_Miss = 100 - ((sample_size * 100)/AE_N)
    
    output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, tvalue, pvalue, R, R.adj, AE_N, sample_size, Perc_Miss)
    
    if (verbose == TRUE) {
    cat("We have collected the following and summarize it in an object:\n")
    cat("Dataset...................:", DATASET, "\n")
    cat("Score/Exposure/biomarker..:", x_name, "\n")
    cat("Trait/outcome.............:", y, "\n")
    cat("Effect size...............:", round(effectsize, 6), "\n")
    cat("Standard error............:", round(SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
    cat("T-value...................:", round(tvalue, 6), "\n")
    cat("P-value...................:", signif(pvalue, 8), "\n")
    cat("R^2.......................:", round(R, 6), "\n")
    cat("Adjusted r^2..............:", round(R.adj, 6), "\n")
    cat("Sample size of AE DB......:", AE_N, "\n")
    cat("Sample size of model......:", sample_size, "\n")
    cat("Missing data %............:", round(Perc_Miss, 6), "\n")
    } else {
      cat("Collecting data in summary object.\n")
    }
  }
  return(output)
  print(output)
}

GLM.BIN <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
  cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' ...\n")
  if (nrow(summary(fit)$coefficients) == 1) {
    output = c(DATASET, x_name, y, rep(NA,9))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data...\n")
    effectsize = summary(fit)$coefficients[2,1]
    SE = summary(fit)$coefficients[2,2]
    OReffect = exp(summary(fit)$coefficients[2,1])
    CI_low = exp(effectsize - 1.96 * SE)
    CI_up = exp(effectsize + 1.96 * SE)
    zvalue = summary(fit)$coefficients[2,3]
    pvalue = summary(fit)$coefficients[2,4]
    dev <- fit$deviance
    nullDev <- fit$null.deviance
    modelN <- length(fit$fitted.values)
    R.l <- 1 - dev / nullDev
    R.cs <- 1 - exp(-(nullDev - dev) / modelN)
    R.n <- R.cs / (1 - (exp(-nullDev/modelN)))
    sample_size = nrow(model.frame(fit))
    AE_N = AEDB.CEA.samplesize
    Perc_Miss = 100 - ((sample_size * 100)/AE_N)
    
    output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, zvalue, pvalue, R.l, R.cs, R.n, AE_N, sample_size, Perc_Miss)
    if (verbose == TRUE) {
    cat("We have collected the following and summarize it in an object:\n")
    cat("Dataset...................:", DATASET, "\n")
    cat("Score/Exposure/biomarker..:", x_name, "\n")
    cat("Trait/outcome.............:", y, "\n")
    cat("Effect size...............:", round(effectsize, 6), "\n")
    cat("Standard error............:", round(SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
    cat("Z-value...................:", round(zvalue, 6), "\n")
    cat("P-value...................:", signif(pvalue, 8), "\n")
    cat("Hosmer and Lemeshow r^2...:", round(R.l, 6), "\n")
    cat("Cox and Snell r^2.........:", round(R.cs, 6), "\n")
    cat("Nagelkerke's pseudo r^2...:", round(R.n, 6), "\n")
    cat("Sample size of AE DB......:", AE_N, "\n")
    cat("Sample size of model......:", sample_size, "\n")
    cat("Missing data %............:", round(Perc_Miss, 6), "\n")
    } else {
      cat("Collecting data in summary object.\n")
    }
  }
  return(output)
  print(output)
}
```


# Background

Using a Mendelian Randomization approach, we recently examined associations between the circulating levels of 41 cytokines and growth factors and the risk of stroke in the MEGASTROKE GWAS dataset (67,000 stroke cases and 450,000 controls) and found Monocyte chemoattractant protein-1 (MCP-1) as the cytokine showing the strongest association with stroke, particularly large artery and cardioembolic stroke (Georgakis et al., 2019a). Genetically elevated MCP-1 levels were also associated with a higher risk of coronary artery disease and myocardial infarction (Georgakis et al., 2019a). Further, in a meta-analysis of 6 observational population-based of longitudinal cohort studies we recently showed that baseline levels of MCP-1 were associated with a higher risk of ischemic stroke over follow-up (Georgakis et al., 2019b).
While these data suggest a central role of MCP-1 in the pathogenesis of atherosclerosis, it remains unknown if MCP-1 levels in the blood really reflect MCP-1 activity. MCP-1 is expressed in the atherosclerotic plaque and attracts monocytes in the subendothelial space (Nelken et al., 1991; Papadopoulou et al., 2008; Takeya et al., 1993; Wilcox et al., 1994). Thus, MCP-1 levels in the plaque might more strongly reflect MCP-1 signaling. However, it remains unknown if MCP-1 plaque levels associate with plaque vulnerability or risk of cardiovascular events.


## Objectives

Against this background we now aim to make use of the data from Athero-Express Biobank Study to explore the associations of MCP-1 protein levels in the atherosclerotic plaques from patients undergoing carotid endarterectomy with phenotypes of plaque vulnerability and secondary vascular events over a follow-up of three years.


## Methods

*Blood*

OLINK-platform 

- IL6: Interleukin 6. Entrez Gene: 3569. OLINK, plasma <!-- Bender MedSystems; cat.nr.: BMS810FF. Recalculated FACS. [pg/mL] -->
- MCP1: Monocyte chemotactic protein 1, MCP-1 (Chemokine (C-C motif) ligand 2, CCL2). Entrez Gene: 6347. OLINK, plasma <!-- Measured at the WKZ. Recalculated Luminex. [pg/mL] -->

> THESE DATA ARE NOT AVAILABLE YET

*Plaque*

Luminex-platform, measured by Luminex

- MCP1: Monocyte chemotactic protein 1 (a.k.a. CCL2; Entrez Gene: 6347) concentration in plaque [pg/ug]. Measured in two experiments, variables `MCP1` and `MCP1_pg_ug_2015`. We consider the latter the best possible measurement as this was corrected for plaque total protein concentration.
- IL6: Interleuking 6 (IL6; Entrez Gene: 3569) concentration in plaque [pg/ug].
- IL6R: Interleuking 6 receptor (IL6R; Entrez Gene: 3570) concentration in plaque [pg/ug].

FACS platform

- IL6: Interleukin 6. Entrez Gene: 3569. Bender MedSystems; cat.nr.: BMS810FF. Recalculated FACS. [pg/mL]


# Loading data

## Clinical data

Loading Athero-Express clinical data.
```{r LoadAEDB}
require(haven)

# AEDB <- haven::read_sav(paste0(AEDB_loc, "/2019-3NEW_AtheroExpressDatabase_ScientificAE_02072019_IC_added.sav"))
AEDB <- haven::read_sav(paste0(AEDB_loc, "/2020_1_NEW_AtheroExpressDatabase_ScientificAE_16-03-2020.sav"))

head(AEDB)



```

#### Examine AEDB

We can examine the contents of the Athero-Express Biobank dataset to know what each variable is called, what class (type) it has, and what the variable description is. 

There is an excellent post on this: https://www.r-bloggers.com/working-with-spss-labels-in-r/. 
```{r AEDB: describe}
AEDB %>% sjPlot::view_df(show.type = TRUE,
                         show.frq = TRUE,
                         show.prc = TRUE,
                         show.na = TRUE, 
                         max.len = TRUE, 
                         wrap.labels = 20,
                         verbose = FALSE, 
                         use.viewer = FALSE,
                         file = paste0(OUT_loc, "/", Today, ".AEDB.dictionary.html")) 
```


## Fixing and creating variables

We need to be very strict in defining _symptoms._ Therefore we will fix a new variable that groups _symptoms_ at inclusion.

Coding of _symptoms_ is as follows:

- missing	-999	
- Asymptomatic	0	
- TIA	1	
- minor stroke	2	
- Major stroke	3	
- Amaurosis fugax	4	
- Four vessel disease	5	
- Vertebrobasilary TIA	7	
- Retinal infarction	8	
- Symptomatic, but aspecific symtoms	9
- Contralateral symptomatic occlusion	10	
- retinal infarction	11	
- armclaudication due to occlusion subclavian artery, CEA needed for bypass	12	
- retinal infarction + TIAs	13	
- Ocular ischemic syndrome	14	
- ischemisch glaucoom	15	
- subclavian steal syndrome	16	
- TGA	17

We will group as follows in `Symptoms.5G`:

1. Asymptomatic > 0
2. TIA > 1, 7, 13
3. Stroke > 2, 3
4. Ocular > 4, 14, 15
5. Retinal infarction > 8, 11
6. Other > 5, 9, 10, 12, 16, 17

We will also group as follows in `AsymptSympt`:

1. Asymptomatic > 0
2. TIA > 1, 7, 13 + Stroke > 2, 3 
3. Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17

We will also group as follows in `AsymptSympt2G`:

1. Asymptomatic > 0
2. TIA > 1, 7, 13 + Stroke > 2, 3 Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17


```{r FixSymptoms, message=FALSE, warning=FALSE}
# Fix symptoms

attach(AEDB)

AEDB$sympt[is.na(AEDB$sympt)] <- -999

# Symptoms.5G
AEDB[,"Symptoms.5G"] <- NA
# AEDB$Symptoms.5G[sympt == "NA"] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == -999] <- NA
AEDB$Symptoms.5G[sympt == 0] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == 1 | sympt == 7 | sympt == 13] <- "TIA"
AEDB$Symptoms.5G[sympt == 2 | sympt == 3] <- "Stroke"
AEDB$Symptoms.5G[sympt == 4 | sympt == 14 | sympt == 15 ] <- "Ocular"
AEDB$Symptoms.5G[sympt == 8 | sympt == 11] <- "Retinal infarction"
AEDB$Symptoms.5G[sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Other"

# AsymptSympt
AEDB[,"AsymptSympt"] <- NA
AEDB$AsymptSympt[sympt == -999] <- NA
AEDB$AsymptSympt[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3] <- "Symptomatic"
AEDB$AsymptSympt[sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Ocular and others"

# AsymptSympt
AEDB[,"AsymptSympt2G"] <- NA
AEDB$AsymptSympt2G[sympt == -999] <- NA
AEDB$AsymptSympt2G[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt2G[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3 | sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Symptomatic"

detach(AEDB)

# table(AEDB$sympt, useNA = "ifany")
# table(AEDB$AsymptSympt2G, useNA = "ifany")
# table(AEDB$Symptoms.5G, useNA = "ifany")
# 
# table(AEDB$AsymptSympt2G, AEDB$sympt, useNA = "ifany")
# table(AEDB$Symptoms.5G, AEDB$sympt, useNA = "ifany")
table(AEDB$AsymptSympt2G, AEDB$Symptoms.5G, useNA = "ifany")

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "sympt", "Symptoms.5G", "AsymptSympt"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# table(AEDB.temp$Symptoms.5G, AEDB.temp$AsymptSympt)
# 
# rm(AEDB.temp)

```

We will also fix the _plaquephenotypes_ variable.  

Coding of symptoms is as follows:

- missing	-999	
- not relevant -888
- fibrous	1	
- fibroatheromatous	2	
- atheromatous	3	


```{r FixPlaquePhenotypes, message=FALSE, warning=FALSE}

# Fix plaquephenotypes
attach(AEDB)
AEDB[,"OverallPlaquePhenotype"] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == 1] <- "fibrous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 2] <- "fibroatheromatous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 3] <- "atheromatous"
detach(AEDB)

table(AEDB$OverallPlaquePhenotype)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "plaquephenotype", "OverallPlaquePhenotype"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

```

We will also fix the _diabetes_ status variable. We define diabetes as history of a diagnosis and/or use of glucose-lowering medications.

```{r FixDiabetes, message=FALSE, warning=FALSE}
# Fix diabetes
attach(AEDB)
AEDB[,"DiabetesStatus"] <- NA
AEDB$DiabetesStatus[DM.composite == -999] <- NA
AEDB$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB)

table(AEDB$DM.composite)

table(AEDB$DiabetesStatus)


# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

```


We will also fix the _smoking_ status variable. We are interested in whether someone never, ever or is currently (at the time of inclusion) smoking. This is based on the questionnaire. 

- `diet801`: are you a smoker?
- `diet802`: did you smoke in the past?

We already have some variables indicating smoking status:

- `SmokingReported`: patient has reported to smoke.
- `SmokingYearOR`: smoking in the year of surgery?
- `SmokerCurrent`: currently smoking?



```{r FixSmoking, message=FALSE, warning=FALSE}
require(labelled)
AEDB$diet801 <- to_factor(AEDB$diet801)
AEDB$diet802 <- to_factor(AEDB$diet802)
AEDB$diet805 <- to_factor(AEDB$diet805)
AEDB$SmokingReported <- to_factor(AEDB$SmokingReported)
AEDB$SmokerCurrent <- to_factor(AEDB$SmokerCurrent)
AEDB$SmokingYearOR <- to_factor(AEDB$SmokingYearOR)

# table(AEDB$diet801)
# table(AEDB$diet802)
# table(AEDB$SmokingReported)
# table(AEDB$SmokerCurrent)
# table(AEDB$SmokingYearOR)
# table(AEDB$SmokingReported, AEDB$SmokerCurrent, useNA = "ifany", dnn = c("Reported smoking", "Current smoker"))
# 
# table(AEDB$diet801, AEDB$diet802, useNA = "ifany", dnn = c("Smoker", "Past smoker"))

cat("\nFixing smoking status.\n")
attach(AEDB)
AEDB[,"SmokerStatus"] <- NA
AEDB$SmokerStatus[diet802 == "don't know"] <- "Never smoked"
AEDB$SmokerStatus[diet802 == "I still smoke"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "no"] <- "Never smoked"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "yes"] <- "Ex-smoker"
AEDB$SmokerStatus[SmokerCurrent == "yes"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no data available/missing"] <- NA
# AEDB$SmokerStatus[is.na(SmokerCurrent)] <- "Never smoked"
detach(AEDB)

cat("\n* Current smoking status.\n")
table(AEDB$SmokerCurrent,
      useNA = "ifany", 
      dnn = c("Current smoker"))

cat("\n* Updated smoking status.\n")
table(AEDB$SmokerStatus,
      useNA = "ifany", 
      dnn = c("Updated smoking status"))

cat("\n* Comparing to 'SmokerCurrent'.\n")
table(AEDB$SmokerStatus, AEDB$SmokerCurrent, 
      useNA = "ifany", 
      dnn = c("Updated smoking status", "Current smoker"))

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)


```

We will also fix the _alcohol_ status variable.


```{r FixAlcohol, message=FALSE, warning=FALSE}

# Fix diabetes
attach(AEDB)
AEDB[,"AlcoholUse"] <- NA
AEDB$AlcoholUse[diet810 == -999] <- NA
AEDB$AlcoholUse[diet810 == 0] <- "No"
AEDB$AlcoholUse[diet810 == 1] <- "Yes"
detach(AEDB)

table(AEDB$AlcoholUse)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)


```

We will also fix a history of CAD, stroke or peripheral intervention status variable. This will be based on `CAD_history`, `Stroke_history`, and `Peripheral.interv`

```{r FixCAD_History, message=FALSE, warning=FALSE}

# Fix diabetes
attach(AEDB)
AEDB[,"MedHx_CVD"] <- NA
AEDB$MedHx_CVD[CAD_history == 0 | Stroke_history == 0 | Peripheral.interv == 0] <- "No"
AEDB$MedHx_CVD[CAD_history == 1 | Stroke_history == 1 | Peripheral.interv == 1] <- "yes"
detach(AEDB)

table(AEDB$CAD_history)
table(AEDB$Stroke_history)
table(AEDB$Peripheral.interv)
table(AEDB$MedHx_CVD)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)


```




# Athero-Express Biobank Study

## Baseline characteristics

We are interested in the following variables at baseline.

- Age (years)
- Female sex (N, %)
- Hypertension (N, %)
- SBP (mmHg)
- DBP (mmHg)
- Diabetes mellitus (N, %)
- Total cholesterol levels (mg/dL)
- LDL cholesterol levels (mg/dL)
- HDL cholesterol levels (mg/dL)
- Triglyceride levels (mg/dL)
- Use of statins (N, %)
- Use of antiplatelet drugs (N, %)
- BMI (kg/m²)
- Smoking status (N, %)
  - Never smokers
  - Ex-smokers
  - Current smokers
- History of CAD (N, %)
- History of PAD (N, %)
- Clinical manifestations
  - Asymptomatic
  - Amaurosis fugax
  - TIA
  - Stroke
- eGFR (mL/min/1.73 m²)
- MCP-1 plaque levels (pg/mL) (LUMINEX based, two experiments `MCP1`, and `MCP1_pg_ug_2015`)
- MCP-1 serum levels (pg/mL) (OLINK based)


```{r Baseline AEDB: creation, include = FALSE}
cat("====================================================================================================\n")
cat("SELECTION THE SHIZZLE\n")

### Artery levels
# AEdata$Artery_summary: 
#           value                                                                                   label
# NOT USE - 0 No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA
# USE - 1                                                                  carotid (left & right)
# USE - 2                                               femoral/iliac (left, right or both sides)
# NOT USE - 3                                               other carotid arteries (common, external)
# NOT USE - 4                                   carotid bypass and injury (left, right or both sides)
# NOT USE - 5                                                         aneurysmata (carotid & femoral)
# NOT USE - 6                                                                                   aorta
# NOT USE - 7                                            other arteries (renal, popliteal, vertebral)
# NOT USE - 8                        femoral bypass, angioseal and injury (left, right or both sides)

### AEdata$informedconsent
#           value                                                                                           label
# NOT USE - -999                                                                                         missing
# NOT USE - 0                                                                                        no, died
# USE - 1                                                                                             yes
# USE - 2                                                             yes, health treatment when possible
# USE - 3                                                                        yes, no health treatment
# USE - 4                                                yes, no health treatment, no commercial business
# NOT USE - 5                                                          yes, no tissue, no commerical business
# NOT USE - 6                      yes, no tissue, no questionnaires, no medical info, no commercial business
# USE - 7                             yes, no questionnaires, no health treatment, no commercial business
# USE - 8                                          yes, no questionnaires, health treatment when possible
# NOT USE - 9                  yes, no tissue, no questionnaires, no health treatment, no commerical business
# USE - 10                               yes, no health treatment, no medical info, no commercial business
# NOT USE - 11 yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business
# USE - 12                                                     yes, no questionnaires, no health treatment
# NOT USE - 13                                                             yes, no tissue, no health treatment
# NOT USE - 14                                                               yes, no tissue, no questionnaires
# NOT USE - 15                                                  yes, no tissue, health treatment when possible
# NOT USE - 16                                                                                  yes, no tissue
# USE - 17                                                                     yes, no commerical business
# USE - 18                                     yes, health treatment when possible, no commercial business
# USE - 19                                                    yes, no medical info, no commercial business
# USE - 20                                                                          yes, no questionnaires
# NOT USE - 21                         yes, no tissue, no questionnaires, no health treatment, no medical info
# NOT USE - 22                  yes, no tissue, no questionnaires, no health treatment, no commercial business
# USE - 23                                                                            yes, no medical info
# USE - 24                                                  yes, no questionnaires, no commercial business
# USE - 25                                    yes, no questionnaires, no health treatment, no medical info
# USE - 26                  yes, no questionnaires, health treatment when possible, no commercial business
# USE - 27                                                      yes,  no health treatment, no medical info
# NOT USE - 28                                                                             no, doesn't want to
# NOT USE - 29                                                                              no, unable to sign
# NOT USE - 30                                                                                 no, no reaction
# NOT USE - 31                                                                                        no, lost
# NOT USE - 32                                                                                     no, too old
# NOT USE - 34                                            yes, no medical info, health treatment when possible
# NOT USE - 35                                             no (never asked for IC because there was no tissue)
# USE - 36                    yes, no medical info, no commercial business, health treatment when possible
# NOT USE - 37                                                                                    no, endpoint
# USE - 38                                                         wil niets invullen, wel alles gebruiken
# USE - 39                                           second informed concents: yes, no commercial business
# NOT USE - 40                                                                              nooit geincludeerd

cat("- sanity checking PRIOR to selection")
library(data.table)
ae.gender <- ifelse(AEDB$Gender == 0, "Female", "Male")
ae.hospital <- ifelse(AEDB$Hospital == 1, "Antonius", "UMCU")
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"))
ae.gender <- ifelse(AEDB$Gender == 0, "Female", "Male")
table(ae.gender, AEDB$Artery_summary, dnn = c("Sex", "Artery"))
# table(ae.gender, AEDB$informedconsent, dnn = c("Sex", "IC"))

rm(ae.gender, ae.hospital)

# I change numeric and factors manually because, well, I wouldn't know how to fix it otherwise
# to have this 'tibble' work with 'tableone'... :-)

AEDB$Age <- as.numeric(AEDB$Age)
AEDB$diastoli <- as.numeric(AEDB$diastoli)
AEDB$systolic <- as.numeric(AEDB$systolic)

AEDB$TC_finalCU <- as.numeric(AEDB$TC_finalCU)
AEDB$LDL_finalCU <- as.numeric(AEDB$LDL_finalCU)
AEDB$HDL_finalCU <- as.numeric(AEDB$HDL_finalCU)
AEDB$TG_finalCU <- as.numeric(AEDB$TG_finalCU)

AEDB$TC_final <- as.numeric(AEDB$TC_final)
AEDB$LDL_final <- as.numeric(AEDB$LDL_final)
AEDB$HDL_final <- as.numeric(AEDB$HDL_final)
AEDB$TG_final <- as.numeric(AEDB$TG_final)

AEDB$Age <- as.numeric(AEDB$Age)
AEDB$GFR_MDRD <- as.numeric(AEDB$GFR_MDRD)
AEDB$BMI <- as.numeric(AEDB$BMI)
AEDB$eCigarettes <- as.numeric(AEDB$eCigarettes)
AEDB$ePackYearsSmoking <- as.numeric(AEDB$ePackYearsSmoking)
AEDB$EP_composite_time <- as.numeric(AEDB$EP_composite_time)

AEDB$macmean0 <- as.numeric(AEDB$macmean0)
AEDB$smcmean0 <- as.numeric(AEDB$smcmean0)
AEDB$neutrophils <- as.numeric(AEDB$neutrophils)
AEDB$Mast_cells_plaque <- as.numeric(AEDB$Mast_cells_plaque)
AEDB$vessel_density_averaged <- as.numeric(AEDB$vessel_density_averaged)

# IL6, IL6R, MCP1 measurements
AEDB$IL6 <- as.numeric(AEDB$IL6) # FACS plaque
AEDB$IL6_pg_ug_2015 <- as.numeric(AEDB$IL6_pg_ug_2015) # LUMINEX plaque
AEDB$IL6R_pg_ug_2015 <- as.numeric(AEDB$IL6R_pg_ug_2015) # LUMINEX plaque
AEDB$MCP1 <- as.numeric(AEDB$MCP1) # LUMINEX plaque
AEDB$MCP1_pg_ug_2015 <- as.numeric(AEDB$MCP1_pg_ug_2015) # LUMINEX plaque
AEDB$hsCRP_plasma <- as.numeric(AEDB$hsCRP_plasma) # LUMINEX

require(labelled)
AEDB$ORyear <- to_factor(AEDB$ORyear)
AEDB$Gender <- to_factor(AEDB$Gender)
AEDB$Hospital <- to_factor(AEDB$Hospital)
AEDB$KDOQI <- to_factor(AEDB$KDOQI)
AEDB$BMI_WHO <- to_factor(AEDB$BMI_WHO)
AEDB$DiabetesStatus <- to_factor(AEDB$DiabetesStatus)
AEDB$SmokerStatus <- to_factor(AEDB$SmokerStatus)
AEDB$AlcoholUse <- to_factor(AEDB$AlcoholUse)

AEDB$Hypertension.selfreport <- to_factor(AEDB$Hypertension1)
AEDB$Hypertension.selfreportdrug <- to_factor(AEDB$Hypertension2)
AEDB$Hypertension.composite <- to_factor(AEDB$Hypertension.composite)
AEDB$Hypertension.drugs <- to_factor(AEDB$Hypertension.drugs)

AEDB$Med.anticoagulants <- to_factor(AEDB$Med.anticoagulants)
AEDB$Med.all.antiplatelet <- to_factor(AEDB$Med.all.antiplatelet)
AEDB$Med.Statin.LLD <- to_factor(AEDB$Med.Statin.LLD)

AEDB$Stroke_Dx <- to_factor(AEDB$Stroke_Dx)
AEDB$CAD_history <- to_factor(AEDB$CAD_history)
AEDB$PAOD <- to_factor(AEDB$PAOD)
AEDB$Peripheral.interv <- to_factor(AEDB$Peripheral.interv)
AEDB$MedHx_CVD <- to_factor(AEDB$MedHx_CVD)


AEDB$sympt <- to_factor(AEDB$sympt)
AEDB$Symptoms.3g <- to_factor(AEDB$Symptoms.3g)
AEDB$Symptoms.4g <- to_factor(AEDB$Symptoms.4g)
AEDB$Symptoms.5G <- to_factor(AEDB$Symptoms.5G)
AEDB$AsymptSympt <- to_factor(AEDB$AsymptSympt)
AEDB$AsymptSympt2G <- to_factor(AEDB$AsymptSympt2G)


AEDB$restenos <- to_factor(AEDB$restenos)
AEDB$stenose <- to_factor(AEDB$stenose)
AEDB$EP_composite <- to_factor(AEDB$EP_composite)
AEDB$Macrophages.bin <- to_factor(AEDB$Macrophages.bin)
AEDB$SMC.bin <- to_factor(AEDB$SMC.bin)
AEDB$IPH.bin <- to_factor(AEDB$IPH.bin)
AEDB$Calc.bin <- to_factor(AEDB$Calc.bin)
AEDB$Collagen.bin <- to_factor(AEDB$Collagen.bin)
AEDB$Fat.bin_10 <- to_factor(AEDB$Fat.bin_10)
AEDB$Fat.bin_40 <- to_factor(AEDB$Fat.bin_40)
AEDB$OverallPlaquePhenotype <- to_factor(AEDB$OverallPlaquePhenotype)

AEDB$Artery_summary <- to_factor(AEDB$Artery_summary)

AEDB$informedconsent <- to_factor(AEDB$informedconsent)

AEDB.CEA <- subset(AEDB,
                    (Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)") & # we only want carotids
                       informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
                       informedconsent != "no, died" &
                       informedconsent != "yes, no tissue, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no health treatment" &
                       informedconsent != "yes, no tissue, no questionnaires" &
                       informedconsent != "yes, no tissue, health treatment when possible" &
                       informedconsent != "yes, no tissue" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                       informedconsent != "no, doesn't want to" &
                       informedconsent != "no, unable to sign" &
                       informedconsent != "no, no reaction" &
                       informedconsent != "no, lost" &
                       informedconsent != "no, too old" &
                       informedconsent != "yes, no medical info, health treatment when possible" &
                       informedconsent != "no (never asked for IC because there was no tissue)" &
                       informedconsent != "no, endpoint" &
                       informedconsent != "nooit geincludeerd" & 
                     !is.na(AsymptSympt2G))
# AEDB.CEA[1:10, 1:10]
dim(AEDB.CEA)
```

```{r}
cat("===========================================================================================\n")
cat("CREATE BASELINE TABLE\n")

# Baseline table variables
basetable_vars = c("Hospital", "ORyear",
                   "Age", "Gender", 
                   "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   "hsCRP_plasma",
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO",
                   "SmokerStatus", "AlcoholUse",
                   "DiabetesStatus", 
                   "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                   "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                   "restenos", "stenose",
                   "MedHx_CVD", "CAD_history", "PAOD", "Peripheral.interv", 
                   "EP_composite", "EP_composite_time",
                   "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
                   "neutrophils", "Mast_cells_plaque",
                   "IPH.bin", "vessel_density_averaged",
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
                   "IL6", "IL6_pg_ug_2015", "IL6R_pg_ug_2015",
                   "MCP1", "MCP1_pg_ug_2015")

basetable_bin = c("Gender", 
                  "KDOQI", "BMI_WHO",
                  "SmokerStatus", "AlcoholUse",
                  "DiabetesStatus", 
                  "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                  "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                  "restenos", "stenose",
                  "CAD_history", "PAOD", "Peripheral.interv", 
                  "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype")
# basetable_bin

basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_con
```

### All patients
Showing the baseline table of the whole Athero-Express Biobank.

```{r Baseline AEDB: Visualize AEDB}
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:3]
```

### CEA patients

Showing the baseline table of the CEA patients in the Athero-Express Biobank.

```{r Baseline AEDB: Visualize AEDB CEA}
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.CEA.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB.CEA, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:3]
```

### CEA patients with `MCP1_pg_ug_2015`

Showing the baseline table of the CEA patients in the Athero-Express Biobank with `MCP1_pg_ug_2015`.

```{r Baseline AEDB: Visualize subsetCEA}
AEDB.CEA.subset <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015))

AEDB.CEA.subset.AsymptSympt.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:6]
```

### CEA patients with `MCP1_pg_ug_2015` and `MCP1`

Showing the baseline table of the CEA patients in the Athero-Express Biobank with `MCP1_pg_ug_2015` _and_ `MCP1`.

```{r Baseline AEDB: Visualize subsetCEA with MCP1}

AEDB.CEA.subset.combo <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015) | !is.na(MCP1))

AEDB.CEA.subset.combo.tableOne = print(CreateTableOne(vars = basetable_vars,
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset.combo, includeNA = TRUE),
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
                          format = "pf",
                          contDigits = 3)[,1:6]
```

### CEA patients with plasma MCP1 levels

Showing the baseline table of the CEA patients in the Athero-Express Biobank with plasma MCP1 levels.

> NOT AVAILABLE YET

```{r Baseline AEDB: Visualize subsetCEA with serum MCP1}
AEDB.CEA.subset.serum <- subset(AEDB.CEA, !is.na(MCP1_plasma))

AEDB.CEA.subset.serum.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset.serum, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:6]
```


### CEA patients with plasma _and_ plaque MCP1 levels

Showing the baseline table of the CEA patients in the Athero-Express Biobank with both plasma and plaque MCP1 levels.

> NOT AVAILABLE YET

```{r Baseline AEDB: Visualize subsetCEA with serum MCP1 and plaque MCP1}
AEDB.CEA.subset.both <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015) & !is.na(MCP1))

AEDB.CEA.subset.both.tableOne = print(CreateTableOne(vars = basetable_vars,
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset.both, includeNA = TRUE),
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
                          format = "pf",
                          contDigits = 3)[,1:6]
```

Writing the baseline table to Excel format. 
```{r Baseline AEDB: write}
# Write basetable
require(openxlsx)

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.xlsx"),
           AEDB.CEA.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "wholeAEDB_Baseline")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.AsymptSympt.xlsx"),
           AEDB.CEA.subset.AsymptSympt.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "wholeAEDB_Baseline_Sympt")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEA.xlsx"),
           AEDB.CEA.subset.combo.tableOne,
           row.names = TRUE,
           col.names = TRUE,
           sheetName = "subsetAEDB_Baseline")

# write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEAserum.AsymptSympt.xlsx"),
#            AEDB.CEA.subset.serum.tableOne, 
#            row.names = TRUE, 
#            col.names = TRUE, 
#            sheetName = "subsetAEDB_Baseline_serum_Sympt")

```


## Data exploration

Here we inspect the data and when necessary transform quantitative measures. We will inspect the raw, natural log transformed + the smallest measurement, and inverse-normal transformation. 

### MCP1 plaque levels 

We will explore the plaque levels. As noted above, we will use `MCP1_pg_ug_2015`.

```{r DataExploration: MCP1 plaque}

summary(AEDB.CEA$MCP1_pg_ug_2015)

do.call(rbind , by(AEDB.CEA$MCP1_pg_ug_2015, AEDB.CEA$AsymptSympt2G, summary))

```

```{r DataExploration: MCP1 plaque visual}
library(patchwork)
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plaque levels",
                    xlab = "pg/ug", 
                    ggtheme = theme_minimal())

min_MCP1_pg_ug_2015 <- min(AEDB.CEA$MCP1_pg_ug_2015, na.rm = TRUE)
min_MCP1_pg_ug_2015

AEDB.CEA$MCP1_pg_ug_2015_LN <- log(AEDB.CEA$MCP1_pg_ug_2015 + min_MCP1_pg_ug_2015)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    # title = "MCP1 plaque levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())

AEDB.CEA$MCP1_pg_ug_2015_rank <- qnorm((rank(AEDB.CEA$MCP1_pg_ug_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1_pg_ug_2015)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_rank",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = "MCP1 plaque levels",
                    xlab = "inverse-normal transformation pg/ug",
                    ggtheme = theme_minimal())

p1 
p2 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "")  | ggpar(p3, legend = "right")

rm(p1, p2, p3)
```


### MCP1 serum levels 

> NOT AVAILABLE YET

```{r DataExploration: MCP1 serum}

summary(AEDB.CEA$MCP1)

do.call(rbind , by(AEDB.CEA$MCP1, AEDB.CEA$AsymptSympt2G, summary))
do.call(rbind , by(AEDB.CEA.subset.serum$MCP1_pg_ug_2015, AEDB.CEA.subset.serum$AsymptSympt2G, summary))
do.call(rbind , by(AEDB.CEA.subset.serum$MCP1, AEDB.CEA.subset.serum$AsymptSympt2G, summary))

```

```{r DataExploration: MCP1 serum visual}
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 serum levels",
                    xlab = "pg/mL", 
                    ggtheme = theme_minimal())

min_MCP1 <- min(AEDB.CEA$MCP1, na.rm = TRUE)
min_MCP1

AEDB.CEA$MCP1_LN <- log(AEDB.CEA$MCP1 + min_MCP1)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 serum levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())

AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_rank",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = "MCP1 serum levels",
                    xlab = "inverse-normal transformation pg/ug",
                    ggtheme = theme_minimal())

p1 
p2 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "")  | ggpar(p3, legend = "right")

rm(p1, p2, p3)

```

## Preliminary conclusion data exploration

In line with the previous work by [Marios Georgakis](https://www.ahajournals.org/doi/full/10.1161/CIRCRESAHA.119.315380){target="_blank"} we will apply _natural log transformation_ on all proteins and focus the analysis on MCP1 in serum and plaque.

# Analyses

The analyses are focused on three elements: 

1) plaque vulnerability phenotypes
2) clinical status at inclusion (symptoms)
3) secondary clinical outcome during three (3) years of follow-up

## Covariates & other variables

1.  Age (continuous in 1-year increment). [`Age`]
2.  Sex (male vs. female). [`Gender`]
3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [`Hypertension.composite`]
4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes and/or administration of glucose lowering medication). [`DiabetesStatus`]
5.  Smoking (current, ex-, never). [`SmokerStatus`]
6.  LDL-C levels (continuous). [`LDL_final`]
7.  Use of lipid-lowering drugs. [`Med.Statin.LLD`]
8.  Use of antiplatelet drugs. [`Med.all.antiplatelet`]
9.  eGFR (continuous). [`GFR_MDRD`]
10.	BMI (continuous). [`BMI`]
11.	History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [`MedHx_CVD`] combination of [`CAD_history`, `Stroke_history`, `Peripheral.interv`]
12.	Level of stenosis (50-70% vs. 70-99%). [`stenose`]

## Models

We will analyze the data through four different models

- Model 1: adjusted for age and sex
- Model 2: adjusted for age, sex, and additionally adjusted for history hypertension (defined from medical history and/or use of antihypertensive medications), diabetes (defined as history of a diagnosis and/or use of glucose-lowering medications), current smoking, LDL-C levels at time of operation, use of statins, use of antiplatelet agents, eGFR, BMI, history of cardiovascular disease (coronary artery disease, stroke, peripheral artery disease), and level of stenosis (50-70%, 70-90%, 90-99%)

## A. Cross-sectional analysis plaque phenotypes

In the cross-sectional analysis of plaque and serum MCP1, IL6, and IL6R levels we will focus on the following plaque vulnerability phenotypes:

- Percentage of macrophages (continuous trait)
- Percentage of SMCs (continuous trait)
- Number of intraplaque microvessels per 3-4 hotspots (continuous trait)
- Presence of moderate/heavy calcifications (binary trait)
- Presence of moderate/heavy collagen content (binary trait)
- Presence of lipid core no/<10% vs. >10% (binary trait)
- Presence of intraplaque hemorrhage (binary trait)

*Continous traits*
```{r CrossSec: plaques - transformations and visualisations continuous}

# macrophages
cat("Summary of data.\n")
summary(AEDB.CEA$macmean0)

min_macmean <- min(AEDB.CEA$macmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % macrophages: ",min_macmean,".\n"))

AEDB.CEA$Macrophages_LN <- log(AEDB.CEA$macmean0 + min_macmean)

ggpubr::gghistogram(AEDB.CEA, "Macrophages_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "natural log-transformed %", 
                    ggtheme = theme_minimal())

AEDB.CEA$Macrophages_rank <- qnorm((rank(AEDB.CEA$macmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$macmean0)))
ggpubr::gghistogram(AEDB.CEA, "Macrophages_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())

# smooth muscle cells
cat("Summary of data.\n")
summary(AEDB.CEA$macmean0)

min_smcmean <- min(AEDB.CEA$smcmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % smooth muscle cells: ",min_smcmean,".\n"))

AEDB.CEA$SMC_LN <- log(AEDB.CEA$smcmean0 + min_smcmean)

ggpubr::gghistogram(AEDB.CEA, "SMC_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "natural log-transformed %", 
                    ggtheme = theme_minimal())

AEDB.CEA$SMC_rank <- qnorm((rank(AEDB.CEA$smcmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$smcmean0)))
ggpubr::gghistogram(AEDB.CEA, "SMC_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())

# vessel density
cat("Summary of data.\n")
summary(AEDB.CEA$vessel_density_averaged)

min_vesseldensity <- min(AEDB.CEA$vessel_density_averaged, na.rm = TRUE)
min_vesseldensity
cat(paste0("\nMinimum value number of intraplaque neovessels per 3-4 hotspots: ",min_vesseldensity,".\n"))

AEDB.CEA$VesselDensity_LN <- log(AEDB.CEA$vessel_density_averaged + min_vesseldensity)

ggpubr::gghistogram(AEDB.CEA, "VesselDensity_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                    xlab = "natural log-transformed number", 
                    ggtheme = theme_minimal())

AEDB.CEA$VesselDensity_rank <- qnorm((rank(AEDB.CEA$vessel_density_averaged, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$vessel_density_averaged)))
ggpubr::gghistogram(AEDB.CEA, "VesselDensity_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                   xlab = "inverse-rank normalized number", 
                    ggtheme = theme_minimal())
```

*Binary traits*
```{r CrossSec: plaques - transformations and visualisations binary}

# calcification
cat("Summary of data.\n")
summary(AEDB.CEA$Calc.bin)
contrasts(AEDB.CEA$Calc.bin)

AEDB.CEA$CalcificationPlaque <- as.factor(AEDB.CEA$Calc.bin)

df <- AEDB.CEA %>%
  filter(!is.na(CalcificationPlaque)) %>%
  group_by(Gender, CalcificationPlaque) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CalcificationPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Calcification",
                    xlab = "calcification", 
                    ggtheme = theme_minimal())
rm(df)

# collagen
cat("Summary of data.\n")
summary(AEDB.CEA$Collagen.bin)
contrasts(AEDB.CEA$Collagen.bin)

AEDB.CEA$CollagenPlaque <- as.factor(AEDB.CEA$Collagen.bin)

df <- AEDB.CEA %>%
  filter(!is.na(CollagenPlaque)) %>%
  group_by(Gender, CollagenPlaque) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CollagenPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Collagen",
                    xlab = "collagen", 
                    ggtheme = theme_minimal())
rm(df)

# fat 10%
cat("Summary of data.\n")
summary(AEDB.CEA$Fat.bin_10)
contrasts(AEDB.CEA$Fat.bin_10)

AEDB.CEA$Fat10Perc <- as.factor(AEDB.CEA$Fat.bin_10)

df <- AEDB.CEA %>%
  filter(!is.na(Fat10Perc)) %>%
  group_by(Gender, Fat10Perc) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "Fat10Perc", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque fat",
                    xlab = "intraplaque fat", 
                    ggtheme = theme_minimal())
rm(df)

# IPH
cat("Summary of data.\n")
summary(AEDB.CEA$IPH.bin)
contrasts(AEDB.CEA$IPH.bin)

AEDB.CEA$IPH <- as.factor(AEDB.CEA$IPH.bin)

df <- AEDB.CEA %>%
  filter(!is.na(IPH)) %>%
  group_by(Gender, IPH) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "IPH", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque hemorrhage",
                    xlab = "intraplaque hemorrhage", 
                    ggtheme = theme_minimal())
rm(df)

# Symptoms
cat("Summary of data.\n")
summary(AEDB.CEA$AsymptSympt)
contrasts(AEDB.CEA$AsymptSympt)

AEDB.CEA$AsymptSympt <- as.factor(AEDB.CEA$AsymptSympt)

df <- AEDB.CEA %>%
  filter(!is.na(AsymptSympt)) %>%
  group_by(Gender, AsymptSympt) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "AsymptSympt", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Symptoms",
                    xlab = "symptoms", 
                    ggtheme = theme_minimal())
rm(df)

```

In this section we make some variables to assist with analysis.
```{r CrossSec: plaques - setup regression }
AEDB.CEA.samplesize = nrow(AEDB.CEA)
# TRAITS.PROTEIN = c("IL6_LN", "MCP1_LN", "IL6_pg_ug_2015_LN", "IL6R_pg_ug_2015_LN", "MCP1_pg_ug_2015_LN")
# TRAITS.PROTEIN.RANK = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank")
# TRAITS.PROTEIN.RANK = c("MCP1_pg_ug_2015_rank", "MCP1_rank")
TRAITS.PROTEIN.RANK = c("MCP1_pg_ug_2015_rank")

# TRAITS.CON = c("Macrophages_LN", "SMC_LN", "VesselDensity_LN") 
TRAITS.CON.RANK = c("Macrophages_rank", "SMC_rank", "VesselDensity_rank")

TRAITS.BIN = c("CalcificationPlaque", "CollagenPlaque", "Fat10Perc", "IPH")

# "Hospital", 
# "Age", "Gender", 
# "TC_final", "LDL_final", "HDL_final", "TG_final", 
# "systolic", "diastoli", "GFR_MDRD", "BMI", 
# "KDOQI", "BMI_WHO",
# "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
# "DiabetesStatus", "Hypertension.composite", 
# "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
# "Stroke_Dx", "sympt", "Symptoms.5G", "restenos",
# "EP_composite", "EP_composite_time",
# "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
# "neutrophils", "Mast_cells_plaque",
# "IPH.bin", "vessel_density_averaged",
# "Calc.bin", "Collagen.bin", 
# "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
# "IL6_pg_ug_2015", "MCP1_pg_ug_2015", 
# "QC2018_FILTER", "CHIP", "SAMPLE_TYPE",
# "CAD_history", "Stroke_history", "Peripheral.interv",
# "stenose"

# 1.  Age (continuous in 1-year increment). [Age]
# 2.  Sex (male vs. female). [Gender]
# 3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
# 4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
# 5.  Smoking (current, ex-, never). [SmokerCurrent]
# 6.  LDL-C levels (continuous). [LDL_final]
# 7.  Use of lipid-lowering drugs. [Med.Statin.LLD]
# 8.  Use of antiplatelet drugs. [Med.all.antiplatelet]
# 9.  eGFR (continuous). [GFR_MDRD]
# 10.	BMI (continuous). [BMI]
# 11.	History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [MedHx_CVD] combinatino of: [CAD_history, Stroke_history, Peripheral.interv]
# 12.	Level of stenosis (50-70% vs. 70-99%). [stenose]

# Models 
# Model 1: adjusted for age and sex
# Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,

COVARIATES_M1 = c("Age", "Gender")

COVARIATES_M2 = c(COVARIATES_M1,  
               "Hypertension.composite", "DiabetesStatus", 
               "SmokerStatus", 
               # "SmokerCurrent",
               "Med.Statin.LLD", "Med.all.antiplatelet", 
               "GFR_MDRD", "BMI", 
               # "CAD_history", "Stroke_history", "Peripheral.interv", 
               "MedHx_CVD",
               "stenose")

# COVARIATES_M3 = c(COVARIATES_M2, "LDL_final")

# COVARIATES_M4 = c(COVARIATES_M2, "hsCRP_plasma")

# COVARIATES_M5 = c(COVARIATES_M2, "IL6_pg_ug_2015_LN")
# COVARIATES_M5rank = c(COVARIATES_M2, "IL6_pg_ug_2015_rank")

```

### Model 1

In this model we correct for _Age_ and _Gender_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

#### Quantitative plaque traits

Analysis of continuous/quantitative plaque traits as a function of serum/plaque MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

#### Binary plaque traits

Analysis of binary plaque traits as a function of serum/plaque MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender,
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


### Model 2

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

#### Quantitative plaque traits

Analysis of continuous/quantitative plaque traits as a function of serum/plaque MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

#### Binary plaque traits

Analysis of binary plaque traits as a function of serum/plaque MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


## B. Cross-sectional analysis symptoms

We will perform a cross-sectional analysis between plaque and serum MCP1, IL6, and IL6R levels and the 'clinical status' of the plaque in terms of presence of patients' symptoms (symptomatic vs. asymptomatic). The symptoms of interest are:

- stroke
- TIA
- retinal infarction
- amaurosis fugax
- asymptomatic

### Model 1

In this model we correct for _Age_, and _Gender_.


```{r CrossSec: symptoms - logistic regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

### Model 2

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis._.


```{r CrossSec: symptoms - logistic regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                MedHx_CVD + + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


## C. Longitudinal analysis secondary clinical outcome

For the longitudinal analyses of plaque and serum MCP1, IL6, and IL6R levels and secondary cardiovascular events over a three-year follow-up period. 

The _primary outcome_ is defined as "a composite of fatal or non-fatal myocardial infarction, fatal or non-fatal stroke, ruptured aortic aneurysm, fatal cardiac failure, coronary or peripheral interventions, leg amputation due to vascular causes, and cardiovascular death", i.e. major adverse cardiovascular events (MACE). Variable: `epmajor.3years`, these include:
- myocardial infarction (MI)
- cerebral infarction (CVA/stroke)
- cardiovascular death (exact cause to be investigated)
- cerebral bleeding (CVA/stroke)
- fatal myocardial infarction (MI)
- fatal cerebral infarction
- fatal cerebral bleeding
- sudden death
- fatal heart failure
- fatal aneurysm rupture
- other cardiovascular death..

The _secondary outcomes_ will be 

- incidence of fatal or non-fatal stroke (ischemic and bleeding) - variable: `epstroke.3years`, these include:
  - cerebral infarction (CVA/stroke)
  - cerebral bleeding (CVA/stroke)
  - fatal cerebral infarction
  - fatal cerebral bleeding.
- incidence of acute coronary events (fatal or non-fatal myocardial infarction, coronary interventions) - variable: `epcoronary.3years`, these include:
  - myocardial infarction (MI)
  - coronary angioplasty (PCI/PTCA)
  - cardiovascular death (exact cause to be investigated)
  - coronary bypass (CABG)
  - fatal myocardial infarction (MI)
  - sudden death.
- cardiovascular death - variable: `epcvdeath.3years`, these include:
  - cardiovascular death (exact cause to be investigated)
  - fatal myocardial infarction (MI)
  - fatal cerebral infarction
  - fatal cerebral bleeding
  - sudden death
  - fatal heart failure
  - fatal aneurysm rupture
  - other cardiovascular death..

### 30- and 90-days FU events

We will use 3-year follow-up, but we will also calculate 30 days and 90 days follow-up 'time-to-event' variables. On average there are 365.25 days in a year. We can calculate 30-days and 90-days follow-up time based on the three years follow-up. 

```{r Calculate new FU cut-offs: maximum FU}
cutt.off.30days = (1/365.25) * 30
cutt.off.90days = (1/365.25) * 90

# Fix maximum FU of 30 and 90 days
AEDB <- AEDB %>%
  mutate(
    FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
    FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
  ) 

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)

AEDB.CEA <- AEDB.CEA %>%
  mutate(
    FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
    FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
  ) 

AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)

DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)

rm(AEDB.CEA.temp)


```

Here we will calculate the new 30- and 90-days follow-up of the events and their event-times of interest:

- MACE (`epmajor.3years`)
- Stroke (`epstroke.3years`)
- Coronary events (`epcoronary.3years`)
- Cardiovascular death (`epcvdeath.3years`)


```{r Calculate new FU cut-offs: times}
avg_days_in_year = 365.25
cutt.off.30days.scaled <- cutt.off.30days * 365.25
cutt.off.90days.scaled <- cutt.off.90days * 365.25
# Event times
AEDB <- AEDB %>%
  mutate(
    ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
  ) 

AEDB.CEA <- AEDB.CEA %>%
  mutate(
    ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
  ) 

```


```{r Cox-regressions: new times}

attach(AEDB)
AEDB[,"epmajor.30days"] <- AEDB$epmajor.3years
AEDB$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0

AEDB[,"epstroke.30days"] <- AEDB$epstroke.3years
AEDB$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0

AEDB[,"epcoronary.30days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0

AEDB[,"epcvdeath.30days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0

AEDB[,"epmajor.90days"] <- AEDB$epmajor.3years
AEDB$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0

AEDB[,"epstroke.90days"] <- AEDB$epstroke.3years
AEDB$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0

AEDB[,"epcoronary.90days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0

AEDB[,"epcvdeath.90days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0

detach(AEDB)

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
                                      "epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
                                      "epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)

attach(AEDB.CEA)
AEDB.CEA[,"epmajor.30days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epstroke.30days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epcoronary.30days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epcvdeath.30days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epmajor.90days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epstroke.90days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epcoronary.90days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epcvdeath.90days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0

detach(AEDB.CEA)

AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
                                      "epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
                                      "epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)

DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)

rm(AEDB.CEA.temp)



```

### Sanity checks

First we do some sanity checks and inventory the time-to-event and event variables.
```{r Cox-regressions: General}
# Reference: https://bioconductor.org/packages/devel/bioc/vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html
# If you want to suppress warnings and messages when installing/loading packages
# suppressPackageStartupMessages({})
install.packages.auto("survival")
install.packages.auto("survminer")
install.packages.auto("Hmisc")

cat("* Creating function to summarize Cox regression and prepare container for results.")
# Function to get summary statistics from Cox regression model
COX.STAT <- function(coxfit, DATASET, OUTCOME, protein){
  cat("Summarizing Cox regression results for '", protein ,"' and its association to '",OUTCOME,"' in '",DATASET,"'.\n")
  if (nrow(summary(coxfit)$coefficients) == 1) {
    output = c(protein, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    cox.sum <- summary(coxfit)
    cox.effectsize = cox.sum$coefficients[1,1]
    cox.SE = cox.sum$coefficients[1,3]
    cox.HReffect = cox.sum$coefficients[1,2]
    cox.CI_low = exp(cox.effectsize - 1.96 * cox.SE)
    cox.CI_up = exp(cox.effectsize + 1.96 * cox.SE)
    cox.zvalue = cox.sum$coefficients[1,4]
    cox.pvalue = cox.sum$coefficients[1,5]
    cox.sample_size = cox.sum$n
    cox.nevents = cox.sum$nevent
    
    output = c(DATASET, OUTCOME, protein, cox.effectsize, cox.SE, cox.HReffect, cox.CI_low, cox.CI_up, cox.zvalue, cox.pvalue, cox.sample_size, cox.nevents)
    cat("We have collected the following:\n")
    cat("Dataset used..............:", DATASET, "\n")
    cat("Outcome analyzed..........:", OUTCOME, "\n")
    cat("Protein...................:", protein, "\n")
    cat("Effect size...............:", round(cox.effectsize, 6), "\n")
    cat("Standard error............:", round(cox.SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(cox.HReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(cox.CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(cox.CI_up, 3), "\n")
    cat("T-value...................:", round(cox.zvalue, 6), "\n")
    cat("P-value...................:", signif(cox.pvalue, 8), "\n")
    cat("Sample size in model......:", cox.sample_size, "\n")
    cat("Number of events..........:", cox.nevents, "\n")
  }
  return(output)
  print(output)
} 

times = c("ep_major_t_3years", 
          "ep_stroke_t_3years", "ep_coronary_t_3years", "ep_cvdeath_t_3years")

endpoints = c("epmajor.3years", 
              "epstroke.3years", "epcoronary.3years", "epcvdeath.3years")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints){
  require(labelled)
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}

for (eventtime in times){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "year", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPerYear.",eventtime,".pdf"), plot = last_plot())
}

times30 = c("ep_major_t_30days", 
          "ep_stroke_t_30days", "ep_coronary_t_30days", "ep_cvdeath_t_30days")

endpoints30 = c("epmajor.30days", 
              "epstroke.30days", "epcoronary.30days", "epcvdeath.30days")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints30){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times30){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}

for (eventtime in times30){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPer30Days.",eventtime,".pdf"), plot = last_plot())
}

times90 = c("ep_major_t_90days", 
          "ep_stroke_t_90days", "ep_coronary_t_90days", "ep_cvdeath_t_90days")

endpoints90 = c("epmajor.90days", 
              "epstroke.90days", "epcoronary.90days", "epcvdeath.90days")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints90){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times90){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}

for (eventtime in times90){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPer90Days.",eventtime,".pdf"), plot = last_plot())
}


```


### Cox regressions

Let's perform the actual Cox-regressions. We will apply a couple of models: 

- Model 1: adjusted for age and sex
- Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis

#### 3 years follow-up

*MODEL 1*
```{r Cox-regression Analysis: Simple model}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

```

*MODEL 2*
```{r Cox-regression Analysis: MODEL 2}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

```


#### 30-days follow-up

*MODEL 1*
```{r Cox-regression Analysis: Simple model, 30 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.30days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

```

*MODEL 2*
```{r Cox-regression Analysis: MODEL 2, 30 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.30days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

```


#### 90-days follow-up

*MODEL 1*
```{r Cox-regression Analysis: Simple model, 90 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.90days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

```

*MODEL 2*
```{r Cox-regression Analysis: MODEL 2, 90 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.90days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

```



# Correlations

## All biomarkers
We correlated serum and plaque levels of the biomarkers.

```{r CrossSampleType Correlations}

# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)


# Creating matrix - inverse-rank transformation
# --------------------------------
# AEDB.CEA.temp <- subset(AEDB.CEA, 
#                           select = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
# AEDB.CEA.temp <- subset(AEDB.CEA, 
#                           select = c("MCP1_rank", "MCP1_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("MCP1_pg_ug_2015_rank",
                                               TRAITS.BIN, TRAITS.CON.RANK)
                                    )


AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
str(AEDB.CEA.temp)
AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")

# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))


# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}

corr_biomarkers.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank))
DT::datatable(corr_biomarkers.rank.df)

# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}


chart.Correlation.new(AEDB.CEA.matrix.RANK, method = "spearman", histogram = TRUE, pch = 3)


# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
library(GGally)
 
# From the help page:

# ggpairs(AEDB.CEA, 
#         columns = c("MCP1_rank", "MCP1_pg_ug_2015_rank", TRAITS.BIN, TRAITS.CON.RANK), 
#         columnLabels = c("MCP1 (serum)", "MCP1", 
#                          "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density"),
#         method = c("spearman"),
#         # ggplot2::aes(colour = Gender),
#         progress = FALSE) 

ggpairs(AEDB.CEA,
        columns = c("MCP1_pg_ug_2015_rank", TRAITS.BIN, TRAITS.CON.RANK),
        columnLabels = c("MCP1",
                         "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density"),
        method = c("spearman"),
        # ggplot2::aes(colour = Gender),
        progress = FALSE)

```

## Circulating MCP1

Finally, we explored in a sub-sample, where circulating MCP-1 levels are available, the following:

1. A correlation between MCP-1 levels in the plaque and circulating MCP-1 levels
2. Associations of circulating MCP-1 levels with plaque vulnerability characteristics
3. Associations of circulating MCP-1 levels with the status of the plaque in terms of presence of symptoms (symptomatic vs. asymptomatic)
4. Associations of circulating MCP-1 levels with the primary composite endpoint of secondary cardiovascular events.

> NOT AVAILABLE YET

```{r MCP1 Serum Correlations}

# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)


# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("MCP1_rank", 
                                     TRAITS.BIN, TRAITS.CON.RANK, 
                                     "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
                                    )

AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
AEDB.CEA.temp$Symptoms.5G <- as.numeric(AEDB.CEA.temp$Symptoms.5G)
AEDB.CEA.temp$AsymptSympt <- as.numeric(AEDB.CEA.temp$AsymptSympt)
AEDB.CEA.temp$EP_major <- as.numeric(AEDB.CEA.temp$EP_major)
AEDB.CEA.temp$EP_composite <- as.numeric(AEDB.CEA.temp$EP_composite)
# str(AEDB.CEA.temp)
AEDB.CEA.matrix.serum.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers_serum.rank <- round(cor(AEDB.CEA.matrix.serum.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

corr_biomarkers_serum_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.serum.RANK, use = "pairwise.complete.obs", method = "spearman")

# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers_serum.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_serum_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))


# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}


corr_biomarkers_serum.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers_serum.rank, corr_biomarkers_serum_p.rank))
DT::datatable(corr_biomarkers_serum.rank.df)

# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}

chart.Correlation.new(AEDB.CEA.matrix.serum.RANK, method = "spearman", histogram = TRUE, pch = 3)


# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
library(GGally)
 
# From the help page:
ggpairs(AEDB.CEA,
        columns = c("MCP1_rank", TRAITS.BIN, TRAITS.CON.RANK, "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite"), 
        columnLabels = c("MCP1 (serum)", 
                         "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density",
                         "Symptoms", "Symptoms (grouped)", "MACE", "Composite"),
        method = c("spearman"),
        # ggplot2::aes(colour = Gender),
        progress = FALSE) 
```

# Additional figures

## Age and sex
We want to create per-age-group figures. 

- Box and Whisker plot for MCP-1 plaque levels by sex and age group (<55, 55-64, 65-74, 75-84, 85+)
- Box and Whisker plot for MCP-1 serum levels by sex and age group (<55, 55-64, 65-74, 75-84, 85+)
- Scatter plot of the correlation and regression line between MCP-1 levels in plaque (y axis) and serum (x axis).


```{r AgeGroups}
library(dplyr)

AEDB.CEA <- AEDB.CEA %>% mutate(AgeGroup = factor(case_when(Age < 55 ~ "<55",
                                                     Age >= 55  & Age <= 64 ~ "55-64",
                                                     Age >= 65  & Age <= 74 ~ "65-74",
                                                     Age >= 75  & Age <= 84 ~ "75-84",
                                                     Age >= 85 ~ "85+"))) 

AEDB.CEA <- AEDB.CEA %>% mutate(AgeGroupSex = factor(case_when(Age < 55 & Gender == "male" ~ "<55 males" ,
                                                        Age >= 55  & Age <= 64 & Gender == "male"~ "55-64 males",
                                                        Age >= 65  & Age <= 74 & Gender == "male"~ "65-74 males",
                                                        Age >= 75  & Age <= 84 & Gender == "male"~ "75-84 males",
                                                        Age >= 85 & Gender == "male"~ "85+ males",
                                                        Age < 55 & Gender == "female" ~ "<55 females" ,
                                                        Age >= 55  & Age <= 64 & Gender == "female"~ "55-64 females ",
                                                        Age >= 65  & Age <= 74 & Gender == "female"~ "65-74 females",
                                                        Age >= 75  & Age <= 84 & Gender == "female"~ "75-84 females",
                                                        Age >= 85 & Gender == "female"~ "85+ females")))

table(AEDB.CEA$AgeGroup, AEDB.CEA$Gender)
table(AEDB.CEA$AgeGroupSex)

```

Now we can draw some graphs of serum/plaque MCP1 levels per sex and age group.

### Inverse-rank transformed data

#### MCP1 plaque levels

```{r MCP1 per AgeGroup per Sex, ranked}

# ?ggpubr::ggboxplot()

# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
```

#### MCP1 serum levels

> NOT AVAILABLE YET

```{r MCP1 per AgeGroup per Sex, ranked serum}
# compare_means(MCP1_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA,
                  x = c("Gender"),
                  y = "MCP1_rank",
                  xlab = "Gender",
                  ylab = "MCP1 serum [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
                  x = c("AgeGroup"),
                  y = "MCP1_rank",
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 serum [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")

```

### Raw data

Simalarly but now for the raw data as median ± interquartile range.

#### MCP1 plaque levels


```{r MCP1 per AgeGroup per Sex}

# ?ggpubr::ggboxplot()
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Gender",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))

ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))
```

### MCP1 serum levels

> NOT AVAILABLE YET

```{r MCP1 per AgeGroup per Sex, serum}
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1", 
                  xlab = "Gender",
                  ylab = "MCP1 serum [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))

ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 serum [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))
```

## Circulating vs. plaque MCP1 levels

We will also make a nice correlation plot between serum and plaque MCP1 levels.

> NOT AVAILABLE YET

```{r MCP1 correlations, serum vs. plaque}

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1_pg_ug_2015",
                  y = "MCP1", 
                  xlab = "MCP1 plaque [pg/ug]",
                  ylab = "MCP1 serum [pg/mL]",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"), cor.coef.coord = c(8,750))

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1_pg_ug_2015_rank",
                  y = "MCP1_rank", 
                  xlab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  ylab = "MCP1 serum [pg/mL]\n(inverse-rank transformation)",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"), cor.coef.coord = c(2,3))

```

## Plaque vs. plaque MCP1 levels
We will also make a nice correlation plot between the two experiments of plaque MCP1 levels. 

```{r MCP1 correlations, plaque vs. plaque}
AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
summary(AEDB.CEA$MCP1)
summary(AEDB.CEA$MCP1_pg_ug_2015)

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1", 
                  y = "MCP1_pg_ug_2015",
                  xlab = "MCP1 plaque [pg/mL] (exp. no. 1)",
                  ylab = "MCP1 plaque [pg/ug] (exp. no. 2)",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"))

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1_rank", 
                  y = "MCP1_pg_ug_2015_rank",
                  xlab = "MCP1 plaque [pg/mL]\n(INRT,  exp. no. 1)",
                  ylab = "MCP1 plaque [pg/ug]\n(INRT,  exp. no. 2)",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"))

```


## Symptoms
We want to create per-symptom figures. 


```{r SymptomGroups}
library(dplyr)

table(AEDB.CEA$AgeGroup, AEDB.CEA$AsymptSympt2G)
table(AEDB.CEA$Gender, AEDB.CEA$AsymptSympt2G)
table(AEDB.CEA$AsymptSympt2G)

```

Now we can draw some graphs of serum/plaque MCP1 levels per symptom group.
```{r MCP1 per SymptomGroups}

# ?ggpubr::ggboxplot()
my_comparisons <- list(c("Asymptomatic", "Symptomatic"))

p1 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "AsymptSympt2G", y = "MCP1_pg_ug_2015_rank",
                  title = "MCP1 plaque [pg/ug] levels per symptom", 
                  xlab = "Symptoms",
                  ylab = "MCP1 plaque [pg/ug]\n inverse-rank transformation",
                  color = "AsymptSympt2G", 
                  palette = c(uithof_color[16], uithof_color[23]),
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
```

```{r MCP1 per SymptomGroups, serum}
p1 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "AsymptSympt2G", y = "MCP1_rank",
                  title = "MCP1 serum [pg/mL] levels per symptom", 
                  xlab = "Symptoms",
                  ylab = "MCP1 serum [pg/mL]\n inverse-rank transformation",
                  color = "AsymptSympt2G", 
                  palette = c(uithof_color[16], uithof_color[23]),
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
rm(p1)
```

## Forest plots

We would also like to visualize the multivariable analyses results.
```{r load model data}
library(ggplot2)
library(openxlsx)
model1_mcp1 <- read.xlsx(paste0(OUT_loc, "/", Today, ".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"))
model2_mcp1 <- read.xlsx(paste0(OUT_loc, "/", Today, ".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"))
model1_mcp1$model <- "univariate"
model2_mcp1$model <- "multivariate"

models_mcp1 <- rbind(model1_mcp1, model2_mcp1)
models_mcp1

```

```{r forestplot plaque}
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"), 
                           
                           levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
                  cen = c(models_mcp1$OR[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]),
                  low = c(models_mcp1$low95CI[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]),
                  high = c(models_mcp1$up95CI[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]))

fp <- ggplot(data=dat, aes(x=group, y=cen, ymin=low, ymax=high)) +
  geom_pointrange() + 
  geom_hline(yintercept=1, lty=2) +  # add a dotted line at x=1 after flip
  coord_flip() +  # flip coordinates (puts labels on y axis)
  xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
  theme(text = element_text(size=14)) +
  ggtitle("Plaque MCP-1 levels (1 SD increment)") +
  theme_minimal()  # use a white background
print(fp)
rm(fp)
```


```{r forestplot serum}
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"), 
                           
                           levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
                  cen = c(models_mcp1$OR[models_mcp1$Predictor=="MCP1_rank"]),
                  low = c(models_mcp1$low95CI[models_mcp1$Predictor=="MCP1_rank"]),
                  high = c(models_mcp1$up95CI[models_mcp1$Predictor=="MCP1_rank"]))

fp <- ggplot(data=dat, aes(x=group, y=cen, ymin=low, ymax=high)) +
  geom_pointrange() + 
  geom_hline(yintercept=1, lty=2) +  # add a dotted line at x=1 after flip
  coord_flip() +  # flip coordinates (puts labels on y axis)
  xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
  theme(text = element_text(size=14)) +
  ggtitle("Serum MCP-1 levels (1 SD increment)") +
  theme_minimal()  # use a white background
print(fp)
rm(fp)
```

## MCP1 vs. cytokines plaque levels correlations

We will plot the correlations of other cytokine plaque levels to the MCP1 plaque levels. These include:

- IL2
- IL4
- IL5
- IL6
- IL8
- IL9
- IL10
- IL12
- IL13
- IL21
- INFG
- TNFA
- MIF
- MCP1
- MIP1a
- RANTES
- MIG
- IP10
- Eotaxin1
- TARC
- PARC
- MDC
- OPG
- sICAM1
- VEGFA
- TGFB

In addition we will look at three metalloproteinases which were measured using an activity assay. 

- MMP2
- MMP8
- MMP9

The proteins were measured using FACS and LUMINEX. Given the different platforms used (FACS vs. LUMINEX), we will inverse rank-normalize these variables as well to scale them to the same scale as the MCP1 plaque levels.


We will set the measurements that yielded '0' to NA, as it is unlikely that any protein ever has exactly 0 copies. The '0' yielded during the experiment are due to the limits of the detection.

### Prepare data
```{r MCP1 vs Cytokines INRT}
cytokines <- c("IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", "IL13", "IL21", 
               "INFG", "TNFA", "MIF", "MCP1", "MIP1a", "RANTES", "MIG", "IP10", "Eotaxin1", 
               "TARC", "PARC", "MDC", "OPG", "sICAM1", "VEGFA", "TGFB")
metalloproteinases <- c("MMP2", "MMP8", "MMP9")

# fix names
names(AEDB.CEA)[names(AEDB.CEA) == "VEFGA"] <- "VEGFA"


proteins_of_interest <- c(cytokines, metalloproteinases)

proteins_of_interest_rank = unlist(lapply(proteins_of_interest, paste0, "_rank"))


# make variables numerics()
AEDB.CEA <- AEDB.CEA %>%
  mutate_each(funs(as.numeric), proteins_of_interest)
  
for(PROTEIN in 1:length(proteins_of_interest)){

  # UCORBIOGSAqc$Z <- NULL
  var.temp.rank = proteins_of_interest_rank[PROTEIN]
  var.temp = proteins_of_interest[PROTEIN]
  
  cat(paste0("\nSelecting ", var.temp, " and standardising: ", var.temp.rank,".\n"))
  cat(paste0("* changing ", var.temp, " to numeric.\n"))

  # AEDB.CEA <-  AEDB.CEA %>% mutate(AEDB.CEA[,var.temp] == replace(AEDB.CEA[,var.temp], AEDB.CEA[,var.temp]==0, NA))

  AEDB.CEA[,var.temp][AEDB.CEA[,var.temp]==0.000000]=NA

  cat(paste0("* standardising ", var.temp, 
             " (mean: ",round(mean(!is.na(AEDB.CEA[,var.temp])), digits = 6),
             ", n = ",sum(!is.na(AEDB.CEA[,var.temp])),").\n"))
  
  AEDB.CEA <- AEDB.CEA %>%
      mutate_at(vars(var.temp), 
        # list(Z = ~ (AEDB.CEA[,var.temp] - mean(AEDB.CEA[,var.temp], na.rm = TRUE))/sd(AEDB.CEA[,var.temp], na.rm = TRUE))
        list(RANK = ~ qnorm((rank(AEDB.CEA[,var.temp], na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA[,var.temp]))))
      )
  # str(UCORBIOGSAqc$Z)
  cat(paste0("* renaming RANK to ", var.temp.rank,".\n"))
  AEDB.CEA[,var.temp.rank] <- NULL
  names(AEDB.CEA)[names(AEDB.CEA) == "RANK"] <- var.temp.rank
}

# rm(var.temp, var.temp.rank)

```

### Visualize transformations

We will just visualize these transformations.

```{r MCP1 vs Cytokines Histograms}
proteins_of_interest_rank_mcp1 <- c("MCP1_pg_ug_2015_rank", proteins_of_interest_rank)

proteins_of_interest_mcp1 <- c("MCP1_pg_ug_2015", proteins_of_interest)

for(PROTEIN in proteins_of_interest_mcp1){
  cat(paste0("Plotting protein ", PROTEIN, ".\n"))
  
  p1 <- ggpubr::gghistogram(AEDB.CEA, PROTEIN,
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = paste0(PROTEIN, " plaque levels"),
                    xlab = "",
                    ggtheme = theme_minimal())
  print(p1)
  
}


for(PROTEIN in proteins_of_interest_rank_mcp1){
  cat(paste0("Plotting protein ", PROTEIN, ".\n"))
  
  p1 <- ggpubr::gghistogram(AEDB.CEA, PROTEIN,
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = paste0(PROTEIN, " plaque levels"),
                    xlab = "inverse-normal transformation",
                    ggtheme = theme_minimal())
  print(p1)
  
}
  
```

### Correlations

Here we calculate correlations between `MCP1_pg_ug_2015` and 28 other cytokines (including `MCP1` as measured in experiment 1. We use Spearman's test, thus, correlations a given in _rho_. Please note the indications of measurement methods:

- _L_: LUMINEX
- _E_: ELISA
- _a_: activity assay

```{r MCP1 vs Cytokines correlations}
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)

# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c(proteins_of_interest_rank_mcp1)
                                    )

# str(AEDB.CEA.temp)
AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

rename_proteins_of_interest_mcp1 <- c("MCP1 (L, exp2)", 
                                    "IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", 
                                    "IL13 (L)", "IL21 (L)", 
                                    "INFG", "TNFA", "MIF (L)", 
                                    "MCP1 (L, exp1)", "MIP1a (L)", "RANTES (L)", "MIG (L)", "IP10 (L)", 
                                    "Eotaxin1 (L)", "TARC (L)", "PARC (L)", "MDC (L)", 
                                    "OPG (L)", "sICAM1 (L)", "VEGFA (E)", "TGFB (E)", "MMP2 (a)", "MMP8 (a)", "MMP9 (a)")
colnames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_mcp1)
rownames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_mcp1)

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")

# ++++++++++++++++++++++++++++
# flattenCorrMatrix
# ++++++++++++++++++++++++++++
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    row = rownames(cormat)[row(cormat)[ut]],
    column = rownames(cormat)[col(cormat)[ut]],
    cor  =(cormat)[ut],
    p = pmat[ut]
    )
}

corr_biomarkers.rank.df <- flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank)


names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "row"] <- "Cytokine_X"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "column"] <- "CytokineY"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "cor"] <- "SpearmanRho"

DT::datatable(corr_biomarkers.rank.df)

fwrite(corr_biomarkers.rank.df, file = paste0(OUT_loc, "/",Today,".correlation_cytokines.txt"))

```

```{r MCP1 vs Cytokines heatmap}
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
p1 <- ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           tl.cex = 6,
           # xlab = c("MCP1"),
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))
p1
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.png"), plot = last_plot())
rm(p1)

```

While visually actractive we are not necessarily interested in the correlations between all the cytokines, rather of MCP1 with other cytokines only.

```{r MCP1 vs Cytokines barplot}
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "MCP1 (L, exp2)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/29)
p_threshold
p1 <- ggbarplot(temp, x = "CytokineY", y = "SpearmanRho",
          fill = "CytokineY",               # change fill color by cyl
          # color = "white",            # Set bar border colors to white
          palette = uithof_color,            # jco journal color palett. see ?ggpar
          xlab = "Cytokine",
          ylab = expression("Spearman's"~italic(rho)),
          sort.val = "desc",          # Sort the value in dscending order
          sort.by.groups = FALSE,     # Don't sort inside each group
          x.text.angle = 45, # Rotate vertically x axis texts
          cex = 0.8
          )
ggpar(p1, legend = "bottom", 
      legend.title = "") +
  theme(axis.text.x = element_text(size = 9),
        axis.text.y = element_text(size = 9)) 

rm(p1)
```

Another version - problably not good. 
```{r MCP1 vs Cytokines dotchart}

p1 <- ggdotchart(temp, x = "CytokineY", y = "p_log10",
           color = "CytokineY",                                # Color by groups
           palette = uithof_color, # Custom color palette
           xlab = "Cytokine",
           ylab = expression(log[10]~"("~italic(p)~")-value"),
           ylim = c(0, 6),
           sorting = "descending",                       # Sort value in descending order
           add = "segments",                             # Add segments from y = 0 to dots
           rotate = FALSE,                                # Rotate vertically
           # group = "CytokineY",                                # Order by groups
           dot.size = 8,                                 # Large dot size
           label = round(temp$SpearmanRho, digits = 3),                        # Add mpg values as dot labels
           font.label = list(color = "white", size = 8, 
                             vjust = 0.5)                   
           )
ggpar(p1, legend = "bottom", 
      legend.title = "") +
  theme(axis.text.x = element_text(size = 9),
        axis.text.y = element_text(size = 9))

# rm(temp, p1)

```


## MCP1 vs. cytokines plaque levels `lm()`

### Model 1

In this model we correct for _Age_ and _Gender_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of plaque cytokines traits as a function of serum/plaque MCP1 levels.
```{r CrossSec: Cytokines - linear regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(proteins_of_interest_rank)) {
    TRAIT = proteins_of_interest_rank[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
```

```{r CrossSec: Cytokines - linear regression MODEL1 RANK Writing}
DT::datatable(GLM.results)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.MCP1_Plaque.Cytokines_Plaques.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```



### Model 2

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of plaque cytokines as a function of serum/plaque MCP1 levels.
```{r CrossSec: Cytokines - linear regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(proteins_of_interest_rank)) {
    TRAIT = proteins_of_interest_rank[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
```

```{r CrossSec: Cytokines - linear regression MODEL2 RANK, writing}
DT::datatable(GLM.results)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.MCP1_Plaque.Cytokines_Plaques.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```



# Session information

------

    Version:      v1.0.9
    Last update:  2020-06-26
    Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
    Description:  Script to analyse MCP1 from the Ather-Express Biobank Study.
    Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
    
    **MoSCoW To-Do List**
    The things we Must, Should, Could, and Would have given the time we have.
    _M_
    * analysis on serum ASAP
    * linear regression models (model 1 and model 2) of `MCP1_pg_ug_2015` with cytokines DONE
    * check out the difference between the measuremens of `MCP1` and `MCP1_pg_ug_2015` MONDAY
    * double check the plotting of the MACE DONE
    * add the statistics for the correlation of `MCP1_pg_ug_2015` with the cytokines DONE
    
    _S_
    * prettify forest plot
    
    _C_
    
    
    _W_
    
    
    **Changes log**
    * v1.0.9 Added linear regression models for MCP1 vs. cytokines plaque levels. Double checked upload of MACE-plots. Added statistics from correlation (heatmap) to txt-file.
    * v1.0.8 Fixed error in MCP1 serum analysis. It turns out the `MCP1` and `MCP1_pg_ug_2015` variables are _both_ measured in plaque, in two separate experiments, exp. no. 1 and exp. no. 2, respectively. 
    * v1.0.7 Fixed the per Age-group MCP1 Box plots. Added correlations with other cytokines in plaques.
    * v1.0.6 Only analyses and figures that end up in the final manuscript.
    * v1.0.5 Update with 30- and 90-days survival.
    * v1.0.4 Updated with Cox-regressions.
    * v1.0.3 Included more models.
    * v1.0.2 Bugs fixed.
    * v1.0.1 Extended with linear and logistic regressions
    * v1.0.0 Inital version
    

------

```{r eval = TRUE}
sessionInfo()
```

# Saving environment
```{r Saving}
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".sample_selection.RData"))
```

------
<sup>&copy; 1979-2020 Sander W. van der Laan | s.w.vanderlaan-2[at]umcutrecht.nl | [swvanderlaan.github.io](https://swvanderlaan.github.io).</sup>
------

